Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations10000
Missing cells148108
Missing cells (%)26.9%
Duplicate rows9
Duplicate rows (%)0.1%
Total size in memory4.1 MiB
Average record size in memory433.0 B

Variable types

Numeric9
DateTime1
Text12
Categorical31
Boolean2

Alerts

AADHAR VERIFIED has constant value "False"Constant
MOBILE VERIFICATION has constant value "True"Constant
Phone Social Premium.a23games has constant value "0.0"Constant
Phone Social Premium.my11 has constant value "0.0"Constant
Phone Social Premium.rummycircle has constant value "0.0"Constant
Phone Social Premium.yatra has constant value "0.0"Constant
Dataset has 9 (0.1%) duplicate rowsDuplicates
ADDRESS TYPE is highly overall correlated with Application StatusHigh correlation
AGE is highly overall correlated with MARITAL STATUSHigh correlation
APPLIED AMOUNT is highly overall correlated with TOTAL ASSET COSTHigh correlation
ASSET CTG is highly overall correlated with Application Status and 1 other fieldsHigh correlation
Application Status is highly overall correlated with ADDRESS TYPE and 2 other fieldsHigh correlation
EMPLOY CONSTITUTION is highly overall correlated with EMPLOYER TYPEHigh correlation
EMPLOYER TYPE is highly overall correlated with EMPLOY CONSTITUTIONHigh correlation
MARITAL STATUS is highly overall correlated with AGEHigh correlation
PRIMARY ASSET MAKE is highly overall correlated with ASSET CTGHigh correlation
Phone Social Premium.microsoft is highly overall correlated with Phone Social Premium.skypeHigh correlation
Phone Social Premium.skype is highly overall correlated with Phone Social Premium.microsoftHigh correlation
TOTAL ASSET COST is highly overall correlated with APPLIED AMOUNT and 1 other fieldsHigh correlation
Phone Social Premium.housing is highly imbalanced (66.0%)Imbalance
Phone Social Premium.indiamart is highly imbalanced (90.0%)Imbalance
Phone Social Premium.instagram is highly imbalanced (57.5%)Imbalance
Phone Social Premium.isWABusiness is highly imbalanced (55.6%)Imbalance
Phone Social Premium.jeevansaathi is highly imbalanced (71.1%)Imbalance
Phone Social Premium.shaadi is highly imbalanced (87.3%)Imbalance
Phone Social Premium.zoho is highly imbalanced (99.3%)Imbalance
HDB BRANCH STATE has 854 (8.5%) missing valuesMissing
MIDDLE NAME has 7145 (71.5%) missing valuesMissing
LAST NAME has 681 (6.8%) missing valuesMissing
Cibil Score has 4297 (43.0%) missing valuesMissing
TOTAL ASSET COST has 5108 (51.1%) missing valuesMissing
ASSET CTG has 5108 (51.1%) missing valuesMissing
MARITAL STATUS has 4894 (48.9%) missing valuesMissing
ADDRESS TYPE has 3312 (33.1%) missing valuesMissing
EMPLOY CONSTITUTION has 4998 (50.0%) missing valuesMissing
EMPLOYER NAME has 5010 (50.1%) missing valuesMissing
EMPLOYER TYPE has 4998 (50.0%) missing valuesMissing
Pan Name has 1053 (10.5%) missing valuesMissing
vpa has 2787 (27.9%) missing valuesMissing
upi_name has 2789 (27.9%) missing valuesMissing
Phone Social Premium.a23games has 9999 (> 99.9%) missing valuesMissing
Phone Social Premium.amazon has 1916 (19.2%) missing valuesMissing
Phone Social Premium.byjus has 1948 (19.5%) missing valuesMissing
Phone Social Premium.flipkart has 1832 (18.3%) missing valuesMissing
Phone Social Premium.housing has 1776 (17.8%) missing valuesMissing
Phone Social Premium.indiamart has 1775 (17.8%) missing valuesMissing
Phone Social Premium.instagram has 6630 (66.3%) missing valuesMissing
Phone Social Premium.isWABusiness has 8427 (84.3%) missing valuesMissing
Phone Social Premium.jeevansaathi has 1829 (18.3%) missing valuesMissing
Phone Social Premium.jiomart has 9590 (95.9%) missing valuesMissing
Phone Social Premium.microsoft has 1872 (18.7%) missing valuesMissing
Phone Social Premium.my11 has 9998 (> 99.9%) missing valuesMissing
Phone Social Premium.paytm has 1757 (17.6%) missing valuesMissing
Phone Social Premium.rummycircle has 9999 (> 99.9%) missing valuesMissing
Phone Social Premium.shaadi has 1779 (17.8%) missing valuesMissing
Phone Social Premium.skype has 1785 (17.8%) missing valuesMissing
Phone Social Premium.toi has 1943 (19.4%) missing valuesMissing
Phone Social Premium.whatsapp has 8427 (84.3%) missing valuesMissing
Phone Social Premium.yatra has 9991 (99.9%) missing valuesMissing
Phone Social Premium.zoho has 1782 (17.8%) missing valuesMissing
AGE has 373 (3.7%) zerosZeros

Reproduction

Analysis started2024-09-10 10:04:19.986526
Analysis finished2024-09-10 10:04:36.434160
Duration16.45 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

DEALER ID
Real number (ℝ)

Distinct2416
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102936.46
Minimum49849
Maximum202616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:36.572026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum49849
5-th percentile64112.75
Q179953.75
median94631
Q3108463
95-th percentile197368
Maximum202616
Range152767
Interquartile range (IQR)28509.25

Descriptive statistics

Standard deviation37830.984
Coefficient of variation (CV)0.36751783
Kurtosis1.7396214
Mean102936.46
Median Absolute Deviation (MAD)14079
Skewness1.6187716
Sum1.0293646 × 109
Variance1.4311834 × 109
MonotonicityNot monotonic
2024-09-10T15:34:36.749568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112006 75
 
0.8%
88282 51
 
0.5%
77503 39
 
0.4%
79245 38
 
0.4%
94718 37
 
0.4%
73665 36
 
0.4%
97418 34
 
0.3%
79449 34
 
0.3%
106557 34
 
0.3%
109236 33
 
0.3%
Other values (2406) 9589
95.9%
ValueCountFrequency (%)
49849 9
0.1%
50020 3
 
< 0.1%
50731 6
0.1%
50826 8
0.1%
51316 10
0.1%
51916 4
 
< 0.1%
52021 5
0.1%
52355 1
 
< 0.1%
52358 6
0.1%
52548 1
 
< 0.1%
ValueCountFrequency (%)
202616 3
< 0.1%
202611 1
 
< 0.1%
202551 1
 
< 0.1%
202548 1
 
< 0.1%
202493 2
< 0.1%
202490 1
 
< 0.1%
201875 1
 
< 0.1%
201872 1
 
< 0.1%
201871 1
 
< 0.1%
201869 2
< 0.1%
Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2022-07-03 00:00:00
Maximum2022-07-31 00:00:00
2024-09-10T15:34:36.909375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:37.034746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Distinct542
Distinct (%)5.4%
Missing1
Missing (%)< 0.1%
Memory size78.2 KiB
2024-09-10T15:34:37.253426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length10.608261
Min length6

Characters and Unicode

Total characters106072
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)0.5%

Sample

1st rowDELHI-SF
2nd rowPATNA-SF
3rd rowDARJEELING-SF
4th rowSAHARANPUR-SF
5th rowMODASA-SF
ValueCountFrequency (%)
noida-sf 369
 
3.5%
delhi-sf 239
 
2.3%
delhi 234
 
2.2%
hyderabad-sf 210
 
2.0%
dehradun-sf 204
 
1.9%
bangalore-sf 147
 
1.4%
ghaziabad-sf 144
 
1.4%
kanpur-sf 135
 
1.3%
chennai-sf 133
 
1.3%
bhopal-sf 131
 
1.2%
Other values (549) 8544
81.4%
2024-09-10T15:34:37.622350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15627
14.7%
S 12055
11.4%
F 10480
 
9.9%
- 9997
 
9.4%
R 7366
 
6.9%
H 5164
 
4.9%
I 5093
 
4.8%
N 4564
 
4.3%
D 4395
 
4.1%
U 3913
 
3.7%
Other values (17) 27418
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 15627
14.7%
S 12055
11.4%
F 10480
 
9.9%
- 9997
 
9.4%
R 7366
 
6.9%
H 5164
 
4.9%
I 5093
 
4.8%
N 4564
 
4.3%
D 4395
 
4.1%
U 3913
 
3.7%
Other values (17) 27418
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 15627
14.7%
S 12055
11.4%
F 10480
 
9.9%
- 9997
 
9.4%
R 7366
 
6.9%
H 5164
 
4.9%
I 5093
 
4.8%
N 4564
 
4.3%
D 4395
 
4.1%
U 3913
 
3.7%
Other values (17) 27418
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 15627
14.7%
S 12055
11.4%
F 10480
 
9.9%
- 9997
 
9.4%
R 7366
 
6.9%
H 5164
 
4.9%
I 5093
 
4.8%
N 4564
 
4.3%
D 4395
 
4.1%
U 3913
 
3.7%
Other values (17) 27418
25.8%

HDB BRANCH STATE
Categorical

MISSING 

Distinct24
Distinct (%)0.3%
Missing854
Missing (%)8.5%
Memory size78.2 KiB
UTTAR PRADESH
1981 
BIHAR
818 
HARYANA
618 
MADHYA PRADESH
606 
MAHARASHTRA
532 
Other values (19)
4591 

Length

Max length16
Median length13
Mean length9.5040455
Min length5

Characters and Unicode

Total characters86924
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDELHI
2nd rowBIHAR
3rd rowWEST BENGAL
4th rowUTTAR PRADESH
5th rowGUJARAT

Common Values

ValueCountFrequency (%)
UTTAR PRADESH 1981
19.8%
BIHAR 818
 
8.2%
HARYANA 618
 
6.2%
MADHYA PRADESH 606
 
6.1%
MAHARASHTRA 532
 
5.3%
DELHI 511
 
5.1%
TAMIL NADU 500
 
5.0%
RAJASTHAN 452
 
4.5%
JHARKHAND 381
 
3.8%
UTTARAKHAND 374
 
3.7%
Other values (14) 2373
23.7%
(Missing) 854
 
8.5%

Length

2024-09-10T15:34:37.772420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 2696
21.2%
uttar 1981
15.6%
bihar 818
 
6.4%
haryana 618
 
4.9%
madhya 606
 
4.8%
maharashtra 532
 
4.2%
delhi 511
 
4.0%
tamil 500
 
3.9%
nadu 500
 
3.9%
rajasthan 452
 
3.6%
Other values (17) 3484
27.4%

Most occurring characters

ValueCountFrequency (%)
A 19778
22.8%
R 9642
11.1%
H 8175
9.4%
T 7537
 
8.7%
D 5185
 
6.0%
S 5016
 
5.8%
E 4453
 
5.1%
N 3998
 
4.6%
3552
 
4.1%
U 3363
 
3.9%
Other values (12) 16225
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 19778
22.8%
R 9642
11.1%
H 8175
9.4%
T 7537
 
8.7%
D 5185
 
6.0%
S 5016
 
5.8%
E 4453
 
5.1%
N 3998
 
4.6%
3552
 
4.1%
U 3363
 
3.9%
Other values (12) 16225
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 19778
22.8%
R 9642
11.1%
H 8175
9.4%
T 7537
 
8.7%
D 5185
 
6.0%
S 5016
 
5.8%
E 4453
 
5.1%
N 3998
 
4.6%
3552
 
4.1%
U 3363
 
3.9%
Other values (12) 16225
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 19778
22.8%
R 9642
11.1%
H 8175
9.4%
T 7537
 
8.7%
D 5185
 
6.0%
S 5016
 
5.8%
E 4453
 
5.1%
N 3998
 
4.6%
3552
 
4.1%
U 3363
 
3.9%
Other values (12) 16225
18.7%
Distinct4463
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:38.015745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length6.4223
Min length3

Characters and Unicode

Total characters64223
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3258 ?
Unique (%)32.6%

Sample

1st rowSUNIL
2nd rowAMRIT
3rd rowANIMESH
4th rowADITYA
5th rowPARMAR
ValueCountFrequency (%)
mohd 202
 
2.0%
mohammad 149
 
1.5%
rahul 65
 
0.7%
sunil 61
 
0.6%
amit 56
 
0.6%
ram 56
 
0.6%
anil 54
 
0.5%
sanjay 52
 
0.5%
mukesh 51
 
0.5%
santosh 50
 
0.5%
Other values (4453) 9204
92.0%
2024-09-10T15:34:38.391479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 12842
20.0%
H 4880
 
7.6%
N 4706
 
7.3%
R 4634
 
7.2%
I 4409
 
6.9%
S 4388
 
6.8%
M 3680
 
5.7%
E 3558
 
5.5%
D 2758
 
4.3%
U 2683
 
4.2%
Other values (16) 15685
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 12842
20.0%
H 4880
 
7.6%
N 4706
 
7.3%
R 4634
 
7.2%
I 4409
 
6.9%
S 4388
 
6.8%
M 3680
 
5.7%
E 3558
 
5.5%
D 2758
 
4.3%
U 2683
 
4.2%
Other values (16) 15685
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 12842
20.0%
H 4880
 
7.6%
N 4706
 
7.3%
R 4634
 
7.2%
I 4409
 
6.9%
S 4388
 
6.8%
M 3680
 
5.7%
E 3558
 
5.5%
D 2758
 
4.3%
U 2683
 
4.2%
Other values (16) 15685
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 12842
20.0%
H 4880
 
7.6%
N 4706
 
7.3%
R 4634
 
7.2%
I 4409
 
6.9%
S 4388
 
6.8%
M 3680
 
5.7%
E 3558
 
5.5%
D 2758
 
4.3%
U 2683
 
4.2%
Other values (16) 15685
24.4%

MIDDLE NAME
Text

MISSING 

Distinct1264
Distinct (%)44.3%
Missing7145
Missing (%)71.5%
Memory size78.2 KiB
2024-09-10T15:34:38.619230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length5.4658494
Min length1

Characters and Unicode

Total characters15605
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique990 ?
Unique (%)34.7%

Sample

1st rowHARESHBHAI
2nd rowABDUL
3rd rowMOHD
4th rowPANJAB
5th rowK
ValueCountFrequency (%)
kumar 514
 
18.0%
so 101
 
3.5%
singh 96
 
3.4%
ram 43
 
1.5%
k 32
 
1.1%
prasad 31
 
1.1%
lal 26
 
0.9%
prakash 19
 
0.7%
chandra 18
 
0.6%
s 17
 
0.6%
Other values (1254) 1958
68.6%
2024-09-10T15:34:38.997902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3238
20.7%
R 1509
9.7%
M 1147
 
7.4%
H 1106
 
7.1%
S 944
 
6.0%
K 931
 
6.0%
U 927
 
5.9%
N 917
 
5.9%
I 841
 
5.4%
D 584
 
3.7%
Other values (16) 3461
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3238
20.7%
R 1509
9.7%
M 1147
 
7.4%
H 1106
 
7.1%
S 944
 
6.0%
K 931
 
6.0%
U 927
 
5.9%
N 917
 
5.9%
I 841
 
5.4%
D 584
 
3.7%
Other values (16) 3461
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3238
20.7%
R 1509
9.7%
M 1147
 
7.4%
H 1106
 
7.1%
S 944
 
6.0%
K 931
 
6.0%
U 927
 
5.9%
N 917
 
5.9%
I 841
 
5.4%
D 584
 
3.7%
Other values (16) 3461
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3238
20.7%
R 1509
9.7%
M 1147
 
7.4%
H 1106
 
7.1%
S 944
 
6.0%
K 931
 
6.0%
U 927
 
5.9%
N 917
 
5.9%
I 841
 
5.4%
D 584
 
3.7%
Other values (16) 3461
22.2%

LAST NAME
Text

MISSING 

Distinct3101
Distinct (%)33.3%
Missing681
Missing (%)6.8%
Memory size78.2 KiB
2024-09-10T15:34:39.199317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length5.7581286
Min length1

Characters and Unicode

Total characters53660
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2341 ?
Unique (%)25.1%

Sample

1st rowCHANDER
2nd rowKUMAR
3rd rowTHAPA
4th rowSINGH
5th rowAMRUTBHAI
ValueCountFrequency (%)
singh 874
 
9.4%
kumar 718
 
7.7%
yadav 185
 
2.0%
khan 173
 
1.9%
ram 160
 
1.7%
sharma 147
 
1.6%
ali 144
 
1.5%
devi 131
 
1.4%
lal 127
 
1.4%
ansari 88
 
0.9%
Other values (3091) 6572
70.5%
2024-09-10T15:34:39.554767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 11417
21.3%
H 4400
 
8.2%
R 4250
 
7.9%
I 3851
 
7.2%
N 3617
 
6.7%
S 3570
 
6.7%
M 2989
 
5.6%
K 2295
 
4.3%
U 2271
 
4.2%
D 2085
 
3.9%
Other values (16) 12915
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 11417
21.3%
H 4400
 
8.2%
R 4250
 
7.9%
I 3851
 
7.2%
N 3617
 
6.7%
S 3570
 
6.7%
M 2989
 
5.6%
K 2295
 
4.3%
U 2271
 
4.2%
D 2085
 
3.9%
Other values (16) 12915
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 11417
21.3%
H 4400
 
8.2%
R 4250
 
7.9%
I 3851
 
7.2%
N 3617
 
6.7%
S 3570
 
6.7%
M 2989
 
5.6%
K 2295
 
4.3%
U 2271
 
4.2%
D 2085
 
3.9%
Other values (16) 12915
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 11417
21.3%
H 4400
 
8.2%
R 4250
 
7.9%
I 3851
 
7.2%
N 3617
 
6.7%
S 3570
 
6.7%
M 2989
 
5.6%
K 2295
 
4.3%
U 2271
 
4.2%
D 2085
 
3.9%
Other values (16) 12915
24.1%

mobile
Real number (ℝ)

Distinct9772
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6300413 × 109
Minimum6.0000422 × 109
Maximum9.9999771 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:39.708403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.0000422 × 109
5-th percentile6.3759853 × 109
Q17.8948576 × 109
median8.8673766 × 109
Q39.588181 × 109
95-th percentile9.9354016 × 109
Maximum9.9999771 × 109
Range3.9999349 × 109
Interquartile range (IQR)1.6933234 × 109

Descriptive statistics

Standard deviation1.0777173 × 109
Coefficient of variation (CV)0.12487974
Kurtosis-0.5789721
Mean8.6300413 × 109
Median Absolute Deviation (MAD)8.0519667 × 108
Skewness-0.64410529
Sum8.6300413 × 1013
Variance1.1614745 × 1018
MonotonicityNot monotonic
2024-09-10T15:34:39.870787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9813642304 4
 
< 0.1%
9771931048 4
 
< 0.1%
8590213895 4
 
< 0.1%
6397903454 3
 
< 0.1%
9548185517 3
 
< 0.1%
9883872142 3
 
< 0.1%
7248847715 3
 
< 0.1%
9534011151 3
 
< 0.1%
8470999891 3
 
< 0.1%
8121405424 3
 
< 0.1%
Other values (9762) 9967
99.7%
ValueCountFrequency (%)
6000042231 1
< 0.1%
6000124521 1
< 0.1%
6000517400 1
< 0.1%
6000572890 1
< 0.1%
6000753702 1
< 0.1%
6000783846 1
< 0.1%
6000784648 1
< 0.1%
6000811584 1
< 0.1%
6000841988 1
< 0.1%
6000986436 1
< 0.1%
ValueCountFrequency (%)
9999977149 1
< 0.1%
9999943673 1
< 0.1%
9999918919 1
< 0.1%
9999904369 1
< 0.1%
9999865901 1
< 0.1%
9999847408 1
< 0.1%
9999825497 1
< 0.1%
9999785655 1
< 0.1%
9999783428 1
< 0.1%
9999656302 1
< 0.1%

AADHAR VERIFIED
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
10000 
ValueCountFrequency (%)
False 10000
100.0%
2024-09-10T15:34:39.996712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Cibil Score
Text

MISSING 

Distinct267
Distinct (%)4.7%
Missing4297
Missing (%)43.0%
Memory size78.2 KiB
2024-09-10T15:34:40.271901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.041031
Min length1

Characters and Unicode

Total characters17343
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.3%

Sample

1st row726
2nd row737
3rd row713
4th row669
5th row762
ValueCountFrequency (%)
752 105
 
1.8%
726 91
 
1.6%
743 88
 
1.5%
746 87
 
1.5%
767 81
 
1.4%
753 74
 
1.3%
744 72
 
1.3%
734 70
 
1.2%
546 70
 
1.2%
754 66
 
1.2%
Other values (258) 4930
86.0%
2024-09-10T15:34:40.700201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 4763
27.5%
6 3022
17.4%
5 1798
 
10.4%
4 1345
 
7.8%
3 1216
 
7.0%
2 1147
 
6.6%
8 1050
 
6.1%
1 967
 
5.6%
0 906
 
5.2%
9 781
 
4.5%
Other values (8) 348
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 4763
27.5%
6 3022
17.4%
5 1798
 
10.4%
4 1345
 
7.8%
3 1216
 
7.0%
2 1147
 
6.6%
8 1050
 
6.1%
1 967
 
5.6%
0 906
 
5.2%
9 781
 
4.5%
Other values (8) 348
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 4763
27.5%
6 3022
17.4%
5 1798
 
10.4%
4 1345
 
7.8%
3 1216
 
7.0%
2 1147
 
6.6%
8 1050
 
6.1%
1 967
 
5.6%
0 906
 
5.2%
9 781
 
4.5%
Other values (8) 348
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 4763
27.5%
6 3022
17.4%
5 1798
 
10.4%
4 1345
 
7.8%
3 1216
 
7.0%
2 1147
 
6.6%
8 1050
 
6.1%
1 967
 
5.6%
0 906
 
5.2%
9 781
 
4.5%
Other values (8) 348
 
2.0%

MOBILE VERIFICATION
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
True
10000 
ValueCountFrequency (%)
True 10000
100.0%
2024-09-10T15:34:40.811197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2412
Distinct (%)24.1%
Missing4
Missing (%)< 0.1%
Memory size78.2 KiB
2024-09-10T15:34:41.037110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length29.505102
Min length15

Characters and Unicode

Total characters294933
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique748 ?
Unique (%)7.5%

Sample

1st rowV D AUTO WHEELS CHHOTIAL
2nd rowCHANDAN AUTOMOBILES 259 KGS TOWER
3rd rowKN VISION 53HILL CART ROAD
4th rowMAHADEV AUTOMOBILES MANGLAUR
5th rowDWARKESH AUTO SHAMLAJI ROAD
ValueCountFrequency (%)
motors 3146
 
6.6%
automobiles 2268
 
4.7%
road 1918
 
4.0%
p 1526
 
3.2%
l 1481
 
3.1%
auto 1402
 
2.9%
ltd 639
 
1.3%
pvt 616
 
1.3%
rd 580
 
1.2%
nagar 535
 
1.1%
Other values (3921) 33764
70.5%
2024-09-10T15:34:41.492389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37891
12.8%
A 37843
12.8%
O 23047
 
7.8%
R 21356
 
7.2%
S 18463
 
6.3%
T 17016
 
5.8%
I 16272
 
5.5%
E 14379
 
4.9%
M 12793
 
4.3%
L 12472
 
4.2%
Other values (30) 83401
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 294933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37891
12.8%
A 37843
12.8%
O 23047
 
7.8%
R 21356
 
7.2%
S 18463
 
6.3%
T 17016
 
5.8%
I 16272
 
5.5%
E 14379
 
4.9%
M 12793
 
4.3%
L 12472
 
4.2%
Other values (30) 83401
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 294933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37891
12.8%
A 37843
12.8%
O 23047
 
7.8%
R 21356
 
7.2%
S 18463
 
6.3%
T 17016
 
5.8%
I 16272
 
5.5%
E 14379
 
4.9%
M 12793
 
4.3%
L 12472
 
4.2%
Other values (30) 83401
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 294933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37891
12.8%
A 37843
12.8%
O 23047
 
7.8%
R 21356
 
7.2%
S 18463
 
6.3%
T 17016
 
5.8%
I 16272
 
5.5%
E 14379
 
4.9%
M 12793
 
4.3%
L 12472
 
4.2%
Other values (30) 83401
28.3%

TOTAL ASSET COST
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3692
Distinct (%)75.5%
Missing5108
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean97612.518
Minimum51873
Maximum241116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:41.642674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum51873
5-th percentile77793.25
Q188425.5
median94400
Q3103499.25
95-th percentile127658.4
Maximum241116
Range189243
Interquartile range (IQR)15073.75

Descriptive statistics

Standard deviation16745.027
Coefficient of variation (CV)0.1715459
Kurtosis9.5724963
Mean97612.518
Median Absolute Deviation (MAD)7141.5
Skewness2.256537
Sum4.7752044 × 108
Variance2.8039593 × 108
MonotonicityNot monotonic
2024-09-10T15:34:41.799394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98000 13
 
0.1%
106500 12
 
0.1%
98300 11
 
0.1%
104000 10
 
0.1%
94000 10
 
0.1%
90500 9
 
0.1%
102407 9
 
0.1%
89500 9
 
0.1%
89000 8
 
0.1%
98712 8
 
0.1%
Other values (3682) 4793
47.9%
(Missing) 5108
51.1%
ValueCountFrequency (%)
51873 1
< 0.1%
53458 1
< 0.1%
55188 1
< 0.1%
56740 1
< 0.1%
57961 1
< 0.1%
58000 1
< 0.1%
58595 1
< 0.1%
64048 1
< 0.1%
64140 1
< 0.1%
64500 1
< 0.1%
ValueCountFrequency (%)
241116 1
< 0.1%
231970 1
< 0.1%
222307 1
< 0.1%
217155 1
< 0.1%
216943 1
< 0.1%
212824 1
< 0.1%
210508 1
< 0.1%
205958 1
< 0.1%
200372 1
< 0.1%
200118 1
< 0.1%

ASSET CTG
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)0.3%
Missing5108
Missing (%)51.1%
Memory size78.2 KiB
MCECA
1821 
SCECA
1227 
MCEXA
1186 
SCEXA
264 
MCPRA
 
126
Other values (8)
268 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters24460
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMCEXA
2nd rowSCEXA
3rd rowMCECA
4th rowMCEXA
5th rowMCECA

Common Values

ValueCountFrequency (%)
MCECA 1821
 
18.2%
SCECA 1227
 
12.3%
MCEXA 1186
 
11.9%
SCEXA 264
 
2.6%
MCPRA 126
 
1.3%
MCECB 69
 
0.7%
MOECA 67
 
0.7%
MCECC 46
 
0.5%
MCEXB 28
 
0.3%
ESECB 25
 
0.2%
Other values (3) 33
 
0.3%
(Missing) 5108
51.1%

Length

2024-09-10T15:34:41.938340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mceca 1821
37.2%
sceca 1227
25.1%
mcexa 1186
24.2%
scexa 264
 
5.4%
mcpra 126
 
2.6%
mcecb 69
 
1.4%
moeca 67
 
1.4%
mcecc 46
 
0.9%
mcexb 28
 
0.6%
esecb 25
 
0.5%
Other values (3) 33
 
0.7%

Most occurring characters

ValueCountFrequency (%)
C 8100
33.1%
E 4801
19.6%
A 4701
19.2%
M 3353
13.7%
S 1539
 
6.3%
X 1478
 
6.0%
R 149
 
0.6%
P 126
 
0.5%
B 123
 
0.5%
O 67
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 8100
33.1%
E 4801
19.6%
A 4701
19.2%
M 3353
13.7%
S 1539
 
6.3%
X 1478
 
6.0%
R 149
 
0.6%
P 126
 
0.5%
B 123
 
0.5%
O 67
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 8100
33.1%
E 4801
19.6%
A 4701
19.2%
M 3353
13.7%
S 1539
 
6.3%
X 1478
 
6.0%
R 149
 
0.6%
P 126
 
0.5%
B 123
 
0.5%
O 67
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 8100
33.1%
E 4801
19.6%
A 4701
19.2%
M 3353
13.7%
S 1539
 
6.3%
X 1478
 
6.0%
R 149
 
0.6%
P 126
 
0.5%
B 123
 
0.5%
O 67
 
0.3%

ASSET MODEL NO
Real number (ℝ)

Distinct269
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150660.76
Minimum124587
Maximum201897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:42.064390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum124587
5-th percentile129101
Q1139542
median143238
Q3160216
95-th percentile199546
Maximum201897
Range77310
Interquartile range (IQR)20674

Descriptive statistics

Standard deviation20847.995
Coefficient of variation (CV)0.13837707
Kurtosis0.32645714
Mean150660.76
Median Absolute Deviation (MAD)8340
Skewness1.2215523
Sum1.5066076 × 109
Variance4.346389 × 108
MonotonicityNot monotonic
2024-09-10T15:34:42.217800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143240 706
 
7.1%
140208 689
 
6.9%
140212 529
 
5.3%
199546 519
 
5.2%
129101 445
 
4.5%
160832 382
 
3.8%
129102 327
 
3.3%
139542 281
 
2.8%
134121 240
 
2.4%
143241 239
 
2.4%
Other values (259) 5643
56.4%
ValueCountFrequency (%)
124587 8
 
0.1%
124648 53
 
0.5%
124649 16
 
0.2%
124650 172
 
1.7%
129101 445
4.5%
129102 327
3.3%
129823 7
 
0.1%
129870 1
 
< 0.1%
129871 2
 
< 0.1%
133250 9
 
0.1%
ValueCountFrequency (%)
201897 1
 
< 0.1%
201896 1
 
< 0.1%
201534 4
 
< 0.1%
201533 3
 
< 0.1%
201532 2
 
< 0.1%
200863 51
0.5%
200862 27
0.3%
200858 1
 
< 0.1%
200857 5
 
0.1%
200153 32
0.3%

APPLIED AMOUNT
Real number (ℝ)

HIGH CORRELATION 

Distinct1230
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91796.731
Minimum10400
Maximum1420000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:42.362922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10400
5-th percentile66752.25
Q180000
median90000
Q399000
95-th percentile125000
Maximum1420000
Range1409600
Interquartile range (IQR)19000

Descriptive statistics

Standard deviation27000.18
Coefficient of variation (CV)0.29413008
Kurtosis802.86167
Mean91796.731
Median Absolute Deviation (MAD)9000
Skewness19.395137
Sum9.179673 × 108
Variance7.290097 × 108
MonotonicityNot monotonic
2024-09-10T15:34:42.514848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90000 852
 
8.5%
85000 836
 
8.4%
95000 634
 
6.3%
80000 593
 
5.9%
99000 542
 
5.4%
75000 357
 
3.6%
70000 270
 
2.7%
100000 231
 
2.3%
98000 215
 
2.1%
99999 188
 
1.9%
Other values (1220) 5282
52.8%
ValueCountFrequency (%)
10400 1
 
< 0.1%
10900 1
 
< 0.1%
11000 1
 
< 0.1%
12000 1
 
< 0.1%
20000 2
< 0.1%
21000 1
 
< 0.1%
25000 3
< 0.1%
30000 4
< 0.1%
32000 1
 
< 0.1%
35000 2
< 0.1%
ValueCountFrequency (%)
1420000 1
 
< 0.1%
1050000 1
 
< 0.1%
830000 1
 
< 0.1%
350000 1
 
< 0.1%
300000 1
 
< 0.1%
299000 1
 
< 0.1%
250000 4
< 0.1%
244500 1
 
< 0.1%
242000 1
 
< 0.1%
240000 1
 
< 0.1%

PRIMARY ASSET MAKE
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
HERO MOTORS
3618 
HONDA MOTORS
3259 
TVS MOTOR CO
1416 
BAJAJ AUTO INDIA
747 
SUZUKI MOTORCYCLE
485 
Other values (12)
475 

Length

Max length28
Median length25
Mean length12.2495
Min length11

Characters and Unicode

Total characters122495
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHONDA MOTORS
2nd rowHERO MOTORS
3rd rowTVS MOTOR CO
4th rowHERO MOTORS
5th rowHONDA MOTORS

Common Values

ValueCountFrequency (%)
HERO MOTORS 3618
36.2%
HONDA MOTORS 3259
32.6%
TVS MOTOR CO 1416
 
14.2%
BAJAJ AUTO INDIA 747
 
7.5%
SUZUKI MOTORCYCLE 485
 
4.9%
YAMAHA MOTOR 224
 
2.2%
AMO ELECTRIC 68
 
0.7%
OKAYA ELECTRIC 49
 
0.5%
AMPERE MOTORS 26
 
0.3%
OKINAWA ELECTRIC 26
 
0.3%
Other values (7) 82
 
0.8%

Length

2024-09-10T15:34:42.677586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
motors 6909
31.1%
hero 3633
16.3%
honda 3259
14.7%
motor 1640
 
7.4%
tvs 1416
 
6.4%
co 1416
 
6.4%
india 771
 
3.5%
bajaj 747
 
3.4%
auto 747
 
3.4%
suzuki 485
 
2.2%
Other values (18) 1209
 
5.4%

Most occurring characters

ValueCountFrequency (%)
O 27309
22.3%
R 12901
10.5%
12232
10.0%
T 11408
9.3%
M 9352
 
7.6%
S 8837
 
7.2%
A 7224
 
5.9%
H 7151
 
5.8%
E 4644
 
3.8%
N 4102
 
3.3%
Other values (15) 17335
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 27309
22.3%
R 12901
10.5%
12232
10.0%
T 11408
9.3%
M 9352
 
7.6%
S 8837
 
7.2%
A 7224
 
5.9%
H 7151
 
5.8%
E 4644
 
3.8%
N 4102
 
3.3%
Other values (15) 17335
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 27309
22.3%
R 12901
10.5%
12232
10.0%
T 11408
9.3%
M 9352
 
7.6%
S 8837
 
7.2%
A 7224
 
5.9%
H 7151
 
5.8%
E 4644
 
3.8%
N 4102
 
3.3%
Other values (15) 17335
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 27309
22.3%
R 12901
10.5%
12232
10.0%
T 11408
9.3%
M 9352
 
7.6%
S 8837
 
7.2%
A 7224
 
5.9%
H 7151
 
5.8%
E 4644
 
3.8%
N 4102
 
3.3%
Other values (15) 17335
14.2%
Distinct267
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:42.930679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length23.7316
Min length3

Characters and Unicode

Total characters237316
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)0.4%

Sample

1st rowSHINE DRUM BSVI
2nd rowSPLENDOR PLUS SELF DRUM BSVI I3S
3rd rowTVS NTORQ SUPER SQUAD EDITION BSVI
4th rowSPLENDOR+ BLK ACCT SS DRUM I3S BSVI
5th rowDIO STD BSVI
ValueCountFrequency (%)
bsvi 8417
 
17.8%
drum 3338
 
7.0%
splendor 2376
 
5.0%
125 1954
 
4.1%
plus 1944
 
4.1%
disc 1911
 
4.0%
self 1710
 
3.6%
activa 1453
 
3.1%
start 1429
 
3.0%
i3s 1271
 
2.7%
Other values (268) 21613
45.6%
2024-09-10T15:34:43.359372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37456
15.8%
S 27572
 
11.6%
I 17894
 
7.5%
L 12408
 
5.2%
D 11834
 
5.0%
E 11647
 
4.9%
R 11378
 
4.8%
V 11315
 
4.8%
B 10940
 
4.6%
A 10036
 
4.2%
Other values (31) 74836
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37456
15.8%
S 27572
 
11.6%
I 17894
 
7.5%
L 12408
 
5.2%
D 11834
 
5.0%
E 11647
 
4.9%
R 11378
 
4.8%
V 11315
 
4.8%
B 10940
 
4.6%
A 10036
 
4.2%
Other values (31) 74836
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37456
15.8%
S 27572
 
11.6%
I 17894
 
7.5%
L 12408
 
5.2%
D 11834
 
5.0%
E 11647
 
4.9%
R 11378
 
4.8%
V 11315
 
4.8%
B 10940
 
4.6%
A 10036
 
4.2%
Other values (31) 74836
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37456
15.8%
S 27572
 
11.6%
I 17894
 
7.5%
L 12408
 
5.2%
D 11834
 
5.0%
E 11647
 
4.9%
R 11378
 
4.8%
V 11315
 
4.8%
B 10940
 
4.6%
A 10036
 
4.2%
Other values (31) 74836
31.5%
Distinct6349
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:43.591034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length18.7686
Min length11

Characters and Unicode

Total characters187686
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5769 ?
Unique (%)57.7%

Sample

1st rowSUNILSEHRAWAT7355@GMAIL.COM
2nd rowNULL@GMAIL.COM
3rd rowCHETTRIDIKSHA@GMAIL.COM
4th rowADITYA98@GAMIL.COM
5th rowPARMARHARESHBHAI1989@GMAIL.COM
ValueCountFrequency (%)
null@gmail.com 934
 
9.3%
nomail@gmail.com 475
 
4.8%
null123@gmail.com 168
 
1.7%
nomail@nomail.com 136
 
1.4%
nul@gmail.com 106
 
1.1%
nullmail@gmail.com 92
 
0.9%
nill@gmail.com 78
 
0.8%
nil@gmail.com 52
 
0.5%
abc@gmail.com 45
 
0.4%
null@hdbfs.com 44
 
0.4%
Other values (6339) 7870
78.7%
2024-09-10T15:34:43.984078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 23896
12.7%
A 22689
12.1%
L 16049
 
8.6%
I 15133
 
8.1%
O 12392
 
6.6%
G 10645
 
5.7%
C 10421
 
5.6%
. 10257
 
5.5%
@ 10000
 
5.3%
N 7163
 
3.8%
Other values (43) 49041
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 187686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 23896
12.7%
A 22689
12.1%
L 16049
 
8.6%
I 15133
 
8.1%
O 12392
 
6.6%
G 10645
 
5.7%
C 10421
 
5.6%
. 10257
 
5.5%
@ 10000
 
5.3%
N 7163
 
3.8%
Other values (43) 49041
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 187686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 23896
12.7%
A 22689
12.1%
L 16049
 
8.6%
I 15133
 
8.1%
O 12392
 
6.6%
G 10645
 
5.7%
C 10421
 
5.6%
. 10257
 
5.5%
@ 10000
 
5.3%
N 7163
 
3.8%
Other values (43) 49041
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 187686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 23896
12.7%
A 22689
12.1%
L 16049
 
8.6%
I 15133
 
8.1%
O 12392
 
6.6%
G 10645
 
5.7%
C 10421
 
5.6%
. 10257
 
5.5%
@ 10000
 
5.3%
N 7163
 
3.8%
Other values (43) 49041
26.1%

MARITAL STATUS
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4894
Missing (%)48.9%
Memory size78.2 KiB
Married
3833 
Single
1273 

Length

Max length7
Median length7
Mean length6.7506855
Min length6

Characters and Unicode

Total characters34469
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 3833
38.3%
Single 1273
 
12.7%
(Missing) 4894
48.9%

Length

2024-09-10T15:34:44.114385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:44.211219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
married 3833
75.1%
single 1273
 
24.9%

Most occurring characters

ValueCountFrequency (%)
r 7666
22.2%
i 5106
14.8%
e 5106
14.8%
M 3833
11.1%
a 3833
11.1%
d 3833
11.1%
S 1273
 
3.7%
n 1273
 
3.7%
g 1273
 
3.7%
l 1273
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 7666
22.2%
i 5106
14.8%
e 5106
14.8%
M 3833
11.1%
a 3833
11.1%
d 3833
11.1%
S 1273
 
3.7%
n 1273
 
3.7%
g 1273
 
3.7%
l 1273
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 7666
22.2%
i 5106
14.8%
e 5106
14.8%
M 3833
11.1%
a 3833
11.1%
d 3833
11.1%
S 1273
 
3.7%
n 1273
 
3.7%
g 1273
 
3.7%
l 1273
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 7666
22.2%
i 5106
14.8%
e 5106
14.8%
M 3833
11.1%
a 3833
11.1%
d 3833
11.1%
S 1273
 
3.7%
n 1273
 
3.7%
g 1273
 
3.7%
l 1273
 
3.7%

GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Male
8408 
Female
1592 

Length

Max length6
Median length4
Mean length4.3184
Min length4

Characters and Unicode

Total characters43184
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 8408
84.1%
Female 1592
 
15.9%

Length

2024-09-10T15:34:44.328450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:44.435425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 8408
84.1%
female 1592
 
15.9%

Most occurring characters

ValueCountFrequency (%)
e 11592
26.8%
a 10000
23.2%
l 10000
23.2%
M 8408
19.5%
F 1592
 
3.7%
m 1592
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11592
26.8%
a 10000
23.2%
l 10000
23.2%
M 8408
19.5%
F 1592
 
3.7%
m 1592
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11592
26.8%
a 10000
23.2%
l 10000
23.2%
M 8408
19.5%
F 1592
 
3.7%
m 1592
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11592
26.8%
a 10000
23.2%
l 10000
23.2%
M 8408
19.5%
F 1592
 
3.7%
m 1592
 
3.7%

DOB
Real number (ℝ)

Distinct4901
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9813395.7
Minimum1011943
Maximum31122001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:44.554434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1011943
5-th percentile1011979
Q11012000
median7112001
Q316051967
95-th percentile27072000
Maximum31122001
Range30110058
Interquartile range (IQR)15039967

Descriptive statistics

Standard deviation8930653.8
Coefficient of variation (CV)0.91004725
Kurtosis-0.71602091
Mean9813395.7
Median Absolute Deviation (MAD)6100008
Skewness0.70120705
Sum9.8133957 × 1010
Variance7.9756577 × 1013
MonotonicityNot monotonic
2024-09-10T15:34:44.716878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1011996 144
 
1.4%
1011997 125
 
1.2%
1011994 117
 
1.2%
1011995 114
 
1.1%
1011991 109
 
1.1%
1011993 105
 
1.1%
1011990 104
 
1.0%
1011983 97
 
1.0%
1011998 96
 
1.0%
1011988 92
 
0.9%
Other values (4891) 8897
89.0%
ValueCountFrequency (%)
1011943 1
 
< 0.1%
1011955 1
 
< 0.1%
1011959 5
 
0.1%
1011960 3
 
< 0.1%
1011961 3
 
< 0.1%
1011962 6
 
0.1%
1011963 16
0.2%
1011964 9
0.1%
1011965 19
0.2%
1011966 13
0.1%
ValueCountFrequency (%)
31122001 1
< 0.1%
31121999 1
< 0.1%
31121998 1
< 0.1%
31121995 2
< 0.1%
31121994 1
< 0.1%
31121993 1
< 0.1%
31121992 1
< 0.1%
31121991 2
< 0.1%
31121990 2
< 0.1%
31121989 1
< 0.1%

AGE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.7961
Minimum0
Maximum79
Zeros373
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:44.871978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q125
median31
Q339
95-th percentile51
Maximum79
Range79
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.202075
Coefficient of variation (CV)0.3523097
Kurtosis1.1497785
Mean31.7961
Median Absolute Deviation (MAD)7
Skewness-0.15699199
Sum317961
Variance125.48647
MonotonicityNot monotonic
2024-09-10T15:34:45.033164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 499
 
5.0%
25 493
 
4.9%
23 491
 
4.9%
24 486
 
4.9%
22 459
 
4.6%
27 458
 
4.6%
28 437
 
4.4%
21 403
 
4.0%
29 393
 
3.9%
0 373
 
3.7%
Other values (44) 5508
55.1%
ValueCountFrequency (%)
0 373
3.7%
17 1
 
< 0.1%
18 12
 
0.1%
19 19
 
0.2%
20 105
 
1.1%
21 403
4.0%
22 459
4.6%
23 491
4.9%
24 486
4.9%
25 493
4.9%
ValueCountFrequency (%)
79 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%
64 2
 
< 0.1%
63 14
0.1%
62 8
0.1%
61 11
0.1%
60 19
0.2%

ADDRESS TYPE
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)0.2%
Missing3312
Missing (%)33.1%
Memory size78.2 KiB
Self/Spouse Owned
2273 
RESIDENCE
2176 
Parental
1788 
Rented
417 
Company Provided
 
10
Other values (7)
 
24

Length

Max length18
Median length17
Mean length11.297398
Min length6

Characters and Unicode

Total characters75557
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowParental
2nd rowSelf/Spouse Owned
3rd rowParental
4th rowRented
5th rowRESIDENCE

Common Values

ValueCountFrequency (%)
Self/Spouse Owned 2273
22.7%
RESIDENCE 2176
21.8%
Parental 1788
17.9%
Rented 417
 
4.2%
Company Provided 10
 
0.1%
PARENT OWNED-HOUSE 9
 
0.1%
OWNED-BUNGLOW 5
 
0.1%
OWNED-PENTHOUSE 4
 
< 0.1%
OWNDED-FLAT 3
 
< 0.1%
OWNED-ROWHOUSE 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 3312
33.1%

Length

2024-09-10T15:34:45.190381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
self/spouse 2273
25.3%
owned 2273
25.3%
residence 2176
24.2%
parental 1788
19.9%
rented 417
 
4.6%
company 10
 
0.1%
provided 10
 
0.1%
parent 10
 
0.1%
owned-house 9
 
0.1%
owned-bunglow 5
 
0.1%
Other values (5) 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 9451
 
12.5%
S 6737
 
8.9%
E 6582
 
8.7%
n 4488
 
5.9%
l 4061
 
5.4%
a 3586
 
4.7%
d 2710
 
3.6%
R 2606
 
3.4%
O 2318
 
3.1%
2293
 
3.0%
Other values (28) 30725
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75557
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9451
 
12.5%
S 6737
 
8.9%
E 6582
 
8.7%
n 4488
 
5.9%
l 4061
 
5.4%
a 3586
 
4.7%
d 2710
 
3.6%
R 2606
 
3.4%
O 2318
 
3.1%
2293
 
3.0%
Other values (28) 30725
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75557
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9451
 
12.5%
S 6737
 
8.9%
E 6582
 
8.7%
n 4488
 
5.9%
l 4061
 
5.4%
a 3586
 
4.7%
d 2710
 
3.6%
R 2606
 
3.4%
O 2318
 
3.1%
2293
 
3.0%
Other values (28) 30725
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75557
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9451
 
12.5%
S 6737
 
8.9%
E 6582
 
8.7%
n 4488
 
5.9%
l 4061
 
5.4%
a 3586
 
4.7%
d 2710
 
3.6%
R 2606
 
3.4%
O 2318
 
3.1%
2293
 
3.0%
Other values (28) 30725
40.7%

EMPLOY CONSTITUTION
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4998
Missing (%)50.0%
Memory size78.2 KiB
SELF-EMPLOYED
3472 
SALARIED
1530 

Length

Max length13
Median length13
Mean length11.470612
Min length8

Characters and Unicode

Total characters57376
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALARIED
2nd rowSELF-EMPLOYED
3rd rowSELF-EMPLOYED
4th rowSALARIED
5th rowSELF-EMPLOYED

Common Values

ValueCountFrequency (%)
SELF-EMPLOYED 3472
34.7%
SALARIED 1530
 
15.3%
(Missing) 4998
50.0%

Length

2024-09-10T15:34:45.319552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:45.421300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
self-employed 3472
69.4%
salaried 1530
30.6%

Most occurring characters

ValueCountFrequency (%)
E 11946
20.8%
L 8474
14.8%
S 5002
8.7%
D 5002
8.7%
F 3472
 
6.1%
- 3472
 
6.1%
M 3472
 
6.1%
P 3472
 
6.1%
O 3472
 
6.1%
Y 3472
 
6.1%
Other values (3) 6120
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 11946
20.8%
L 8474
14.8%
S 5002
8.7%
D 5002
8.7%
F 3472
 
6.1%
- 3472
 
6.1%
M 3472
 
6.1%
P 3472
 
6.1%
O 3472
 
6.1%
Y 3472
 
6.1%
Other values (3) 6120
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 11946
20.8%
L 8474
14.8%
S 5002
8.7%
D 5002
8.7%
F 3472
 
6.1%
- 3472
 
6.1%
M 3472
 
6.1%
P 3472
 
6.1%
O 3472
 
6.1%
Y 3472
 
6.1%
Other values (3) 6120
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 11946
20.8%
L 8474
14.8%
S 5002
8.7%
D 5002
8.7%
F 3472
 
6.1%
- 3472
 
6.1%
M 3472
 
6.1%
P 3472
 
6.1%
O 3472
 
6.1%
Y 3472
 
6.1%
Other values (3) 6120
10.7%

EMPLOYER NAME
Text

MISSING 

Distinct3694
Distinct (%)74.0%
Missing5010
Missing (%)50.1%
Memory size78.2 KiB
2024-09-10T15:34:45.613891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length60
Median length48
Mean length17.331663
Min length3

Characters and Unicode

Total characters86485
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3512 ?
Unique (%)70.4%

Sample

1st rowsehrawat spare parts
2nd rowThapas shop
3rd rowagriculture farming
4th rowONE RAJA
5th rowagriculture
ValueCountFrequency (%)
store 829
 
6.4%
agriculture 666
 
5.1%
shop 454
 
3.5%
ltd 450
 
3.5%
pvt 386
 
3.0%
kirana 348
 
2.7%
general 286
 
2.2%
work 168
 
1.3%
and 119
 
0.9%
milk 113
 
0.9%
Other values (3990) 9182
70.6%
2024-09-10T15:34:46.003657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8048
 
9.3%
a 4636
 
5.4%
r 4553
 
5.3%
A 4378
 
5.1%
e 3727
 
4.3%
R 3710
 
4.3%
E 3115
 
3.6%
t 3024
 
3.5%
i 2976
 
3.4%
T 2820
 
3.3%
Other values (58) 45498
52.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8048
 
9.3%
a 4636
 
5.4%
r 4553
 
5.3%
A 4378
 
5.1%
e 3727
 
4.3%
R 3710
 
4.3%
E 3115
 
3.6%
t 3024
 
3.5%
i 2976
 
3.4%
T 2820
 
3.3%
Other values (58) 45498
52.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8048
 
9.3%
a 4636
 
5.4%
r 4553
 
5.3%
A 4378
 
5.1%
e 3727
 
4.3%
R 3710
 
4.3%
E 3115
 
3.6%
t 3024
 
3.5%
i 2976
 
3.4%
T 2820
 
3.3%
Other values (58) 45498
52.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8048
 
9.3%
a 4636
 
5.4%
r 4553
 
5.3%
A 4378
 
5.1%
e 3727
 
4.3%
R 3710
 
4.3%
E 3115
 
3.6%
t 3024
 
3.5%
i 2976
 
3.4%
T 2820
 
3.3%
Other values (58) 45498
52.6%

EMPLOYER TYPE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.1%
Missing4998
Missing (%)50.0%
Memory size78.2 KiB
SELF-EMPLOYED
3473 
SALARIED
1354 
Non-Government
 
152
Government
 
23

Length

Max length14
Median length13
Mean length11.663135
Min length8

Characters and Unicode

Total characters58339
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALARIED
2nd rowSELF-EMPLOYED
3rd rowSELF-EMPLOYED
4th rowSALARIED
5th rowSELF-EMPLOYED

Common Values

ValueCountFrequency (%)
SELF-EMPLOYED 3473
34.7%
SALARIED 1354
 
13.5%
Non-Government 152
 
1.5%
Government 23
 
0.2%
(Missing) 4998
50.0%

Length

2024-09-10T15:34:46.145995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:46.258983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
self-employed 3473
69.4%
salaried 1354
 
27.1%
non-government 152
 
3.0%
government 23
 
0.5%

Most occurring characters

ValueCountFrequency (%)
E 11773
20.2%
L 8300
14.2%
S 4827
8.3%
D 4827
8.3%
- 3625
 
6.2%
F 3473
 
6.0%
M 3473
 
6.0%
P 3473
 
6.0%
O 3473
 
6.0%
Y 3473
 
6.0%
Other values (12) 7622
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 11773
20.2%
L 8300
14.2%
S 4827
8.3%
D 4827
8.3%
- 3625
 
6.2%
F 3473
 
6.0%
M 3473
 
6.0%
P 3473
 
6.0%
O 3473
 
6.0%
Y 3473
 
6.0%
Other values (12) 7622
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 11773
20.2%
L 8300
14.2%
S 4827
8.3%
D 4827
8.3%
- 3625
 
6.2%
F 3473
 
6.0%
M 3473
 
6.0%
P 3473
 
6.0%
O 3473
 
6.0%
Y 3473
 
6.0%
Other values (12) 7622
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 11773
20.2%
L 8300
14.2%
S 4827
8.3%
D 4827
8.3%
- 3625
 
6.2%
F 3473
 
6.0%
M 3473
 
6.0%
P 3473
 
6.0%
O 3473
 
6.0%
Y 3473
 
6.0%
Other values (12) 7622
13.1%

Pan Name
Text

MISSING 

Distinct7317
Distinct (%)81.8%
Missing1053
Missing (%)10.5%
Memory size78.2 KiB
2024-09-10T15:34:46.478514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length57
Median length41
Mean length13.585671
Min length1

Characters and Unicode

Total characters121551
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6606 ?
Unique (%)73.8%

Sample

1st rowSUNIL KUMAR
2nd rowAMRIT KUMAR
3rd rowANIMESH THAPA
4th rowADITYA KUMAR
5th rowHARESHBHAI AMRUTBHAI PARMAR
ValueCountFrequency (%)
kumar 1495
 
8.0%
singh 644
 
3.5%
khan 174
 
0.9%
devi 162
 
0.9%
mohd 158
 
0.8%
yadav 146
 
0.8%
ram 124
 
0.7%
ali 120
 
0.6%
mohammad 118
 
0.6%
sharma 116
 
0.6%
Other values (6430) 15351
82.5%
2024-09-10T15:34:46.882182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 22918
18.9%
9661
 
7.9%
R 8917
 
7.3%
H 8394
 
6.9%
I 7546
 
6.2%
N 7531
 
6.2%
S 7027
 
5.8%
M 6611
 
5.4%
U 5377
 
4.4%
E 4883
 
4.0%
Other values (44) 32686
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 22918
18.9%
9661
 
7.9%
R 8917
 
7.3%
H 8394
 
6.9%
I 7546
 
6.2%
N 7531
 
6.2%
S 7027
 
5.8%
M 6611
 
5.4%
U 5377
 
4.4%
E 4883
 
4.0%
Other values (44) 32686
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 22918
18.9%
9661
 
7.9%
R 8917
 
7.3%
H 8394
 
6.9%
I 7546
 
6.2%
N 7531
 
6.2%
S 7027
 
5.8%
M 6611
 
5.4%
U 5377
 
4.4%
E 4883
 
4.0%
Other values (44) 32686
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 22918
18.9%
9661
 
7.9%
R 8917
 
7.3%
H 8394
 
6.9%
I 7546
 
6.2%
N 7531
 
6.2%
S 7027
 
5.8%
M 6611
 
5.4%
U 5377
 
4.4%
E 4883
 
4.0%
Other values (44) 32686
26.9%

name
Text

Distinct8964
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2024-09-10T15:34:47.123605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length37
Mean length14.5662
Min length3

Characters and Unicode

Total characters145662
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8380 ?
Unique (%)83.8%

Sample

1st rowSUNIL CHANDER
2nd rowAMRIT KUMAR
3rd rowANIMESH THAPA
4th rowADITYA SINGH
5th rowPARMAR HARESHBHAI AMRUTBHAI
ValueCountFrequency (%)
kumar 1239
 
5.6%
singh 970
 
4.4%
ram 259
 
1.2%
mohd 215
 
1.0%
yadav 185
 
0.8%
mohammad 177
 
0.8%
khan 175
 
0.8%
ali 163
 
0.7%
lal 156
 
0.7%
sharma 148
 
0.7%
Other values (7678) 18487
83.4%
2024-09-10T15:34:47.522505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 27497
18.9%
12174
 
8.4%
R 10393
 
7.1%
H 10386
 
7.1%
N 9240
 
6.3%
I 9101
 
6.2%
S 8902
 
6.1%
M 7816
 
5.4%
U 5881
 
4.0%
E 5800
 
4.0%
Other values (17) 38472
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 27497
18.9%
12174
 
8.4%
R 10393
 
7.1%
H 10386
 
7.1%
N 9240
 
6.3%
I 9101
 
6.2%
S 8902
 
6.1%
M 7816
 
5.4%
U 5881
 
4.0%
E 5800
 
4.0%
Other values (17) 38472
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 27497
18.9%
12174
 
8.4%
R 10393
 
7.1%
H 10386
 
7.1%
N 9240
 
6.3%
I 9101
 
6.2%
S 8902
 
6.1%
M 7816
 
5.4%
U 5881
 
4.0%
E 5800
 
4.0%
Other values (17) 38472
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 27497
18.9%
12174
 
8.4%
R 10393
 
7.1%
H 10386
 
7.1%
N 9240
 
6.3%
I 9101
 
6.2%
S 8902
 
6.1%
M 7816
 
5.4%
U 5881
 
4.0%
E 5800
 
4.0%
Other values (17) 38472
26.4%

vpa
Categorical

MISSING 

Distinct40
Distinct (%)0.6%
Missing2787
Missing (%)27.9%
Memory size78.2 KiB
YBL
1451 
OKSBI
1025 
PAYTM
877 
OKAXIS
696 
OKICICI
673 
Other values (35)
2491 

Length

Max length15
Median length10
Mean length5.0530986
Min length2

Characters and Unicode

Total characters36448
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowABFSPAY
2nd rowOKSBI
3rd rowPAYTM
4th rowIKWIK
5th rowAXL

Common Values

ValueCountFrequency (%)
YBL 1451
14.5%
OKSBI 1025
 
10.2%
PAYTM 877
 
8.8%
OKAXIS 696
 
7.0%
OKICICI 673
 
6.7%
OKHDFCBANK 658
 
6.6%
AXL 549
 
5.5%
IBL 487
 
4.9%
PTYES 188
 
1.9%
IKWIK 99
 
1.0%
Other values (30) 510
 
5.1%
(Missing) 2787
27.9%

Length

2024-09-10T15:34:47.671419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ybl 1451
20.1%
oksbi 1025
14.2%
paytm 877
12.2%
okaxis 696
9.6%
okicici 673
9.3%
okhdfcbank 658
9.1%
axl 549
 
7.6%
ibl 487
 
6.8%
ptyes 188
 
2.6%
ikwik 99
 
1.4%
Other values (30) 510
 
7.1%

Most occurring characters

ValueCountFrequency (%)
I 4737
13.0%
K 3970
10.9%
B 3811
10.5%
O 3081
8.5%
A 3077
8.4%
L 2570
 
7.1%
Y 2547
 
7.0%
S 2130
 
5.8%
C 2112
 
5.8%
P 1378
 
3.8%
Other values (17) 7035
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 4737
13.0%
K 3970
10.9%
B 3811
10.5%
O 3081
8.5%
A 3077
8.4%
L 2570
 
7.1%
Y 2547
 
7.0%
S 2130
 
5.8%
C 2112
 
5.8%
P 1378
 
3.8%
Other values (17) 7035
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 4737
13.0%
K 3970
10.9%
B 3811
10.5%
O 3081
8.5%
A 3077
8.4%
L 2570
 
7.1%
Y 2547
 
7.0%
S 2130
 
5.8%
C 2112
 
5.8%
P 1378
 
3.8%
Other values (17) 7035
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 4737
13.0%
K 3970
10.9%
B 3811
10.5%
O 3081
8.5%
A 3077
8.4%
L 2570
 
7.1%
Y 2547
 
7.0%
S 2130
 
5.8%
C 2112
 
5.8%
P 1378
 
3.8%
Other values (17) 7035
19.3%

upi_name
Text

MISSING 

Distinct6693
Distinct (%)92.8%
Missing2789
Missing (%)27.9%
Memory size78.2 KiB
2024-09-10T15:34:47.916178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length58
Median length42
Mean length14.944391
Min length3

Characters and Unicode

Total characters107764
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6317 ?
Unique (%)87.6%

Sample

1st rowSUNIL KUMAR
2nd rowAmrit Kumar
3rd rowAditya Kumar
4th rowHARESHBHAI AMRUTBHAI
5th rowAYSHA A
ValueCountFrequency (%)
kumar 1262
 
7.2%
singh 585
 
3.3%
mr 409
 
2.3%
so 344
 
2.0%
khan 163
 
0.9%
s 155
 
0.9%
yadav 130
 
0.7%
md 128
 
0.7%
devi 128
 
0.7%
mohd 121
 
0.7%
Other values (5615) 14051
80.4%
2024-09-10T15:34:48.328759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14926
 
13.9%
12500
 
11.6%
R 6144
 
5.7%
S 5910
 
5.5%
H 5533
 
5.1%
M 5033
 
4.7%
N 4989
 
4.6%
I 4917
 
4.6%
a 3956
 
3.7%
K 3600
 
3.3%
Other values (54) 40256
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 14926
 
13.9%
12500
 
11.6%
R 6144
 
5.7%
S 5910
 
5.5%
H 5533
 
5.1%
M 5033
 
4.7%
N 4989
 
4.6%
I 4917
 
4.6%
a 3956
 
3.7%
K 3600
 
3.3%
Other values (54) 40256
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 14926
 
13.9%
12500
 
11.6%
R 6144
 
5.7%
S 5910
 
5.5%
H 5533
 
5.1%
M 5033
 
4.7%
N 4989
 
4.6%
I 4917
 
4.6%
a 3956
 
3.7%
K 3600
 
3.3%
Other values (54) 40256
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 14926
 
13.9%
12500
 
11.6%
R 6144
 
5.7%
S 5910
 
5.5%
H 5533
 
5.1%
M 5033
 
4.7%
N 4989
 
4.6%
I 4917
 
4.6%
a 3956
 
3.7%
K 3600
 
3.3%
Other values (54) 40256
37.4%

Phone Social Premium.a23games
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size78.2 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 9999
> 99.9%

Length

2024-09-10T15:34:48.464575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:48.559297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Phone Social Premium.amazon
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1916
Missing (%)19.2%
Memory size78.2 KiB
0.0
4151 
1.0
3933 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24252
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 4151
41.5%
1.0 3933
39.3%
(Missing) 1916
19.2%

Length

2024-09-10T15:34:48.662875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:48.770785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4151
51.3%
1.0 3933
48.7%

Most occurring characters

ValueCountFrequency (%)
0 12235
50.4%
. 8084
33.3%
1 3933
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12235
50.4%
. 8084
33.3%
1 3933
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12235
50.4%
. 8084
33.3%
1 3933
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12235
50.4%
. 8084
33.3%
1 3933
 
16.2%

Phone Social Premium.byjus
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1948
Missing (%)19.5%
Memory size78.2 KiB
0.0
6847 
1.0
1205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24156
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6847
68.5%
1.0 1205
 
12.0%
(Missing) 1948
 
19.5%

Length

2024-09-10T15:34:48.883842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:49.002742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6847
85.0%
1.0 1205
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 14899
61.7%
. 8052
33.3%
1 1205
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14899
61.7%
. 8052
33.3%
1 1205
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14899
61.7%
. 8052
33.3%
1 1205
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14899
61.7%
. 8052
33.3%
1 1205
 
5.0%

Phone Social Premium.flipkart
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1832
Missing (%)18.3%
Memory size78.2 KiB
1.0
6113 
0.0
2055 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24504
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 6113
61.1%
0.0 2055
 
20.5%
(Missing) 1832
 
18.3%

Length

2024-09-10T15:34:49.117256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:49.223233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6113
74.8%
0.0 2055
 
25.2%

Most occurring characters

ValueCountFrequency (%)
0 10223
41.7%
. 8168
33.3%
1 6113
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10223
41.7%
. 8168
33.3%
1 6113
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10223
41.7%
. 8168
33.3%
1 6113
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10223
41.7%
. 8168
33.3%
1 6113
24.9%

Phone Social Premium.housing
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1776
Missing (%)17.8%
Memory size78.2 KiB
0.0
7704 
1.0
 
520

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7704
77.0%
1.0 520
 
5.2%
(Missing) 1776
 
17.8%

Length

2024-09-10T15:34:49.338021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:49.442552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7704
93.7%
1.0 520
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 15928
64.6%
. 8224
33.3%
1 520
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15928
64.6%
. 8224
33.3%
1 520
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15928
64.6%
. 8224
33.3%
1 520
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15928
64.6%
. 8224
33.3%
1 520
 
2.1%

Phone Social Premium.indiamart
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1775
Missing (%)17.8%
Memory size78.2 KiB
1.0
8118 
0.0
 
107

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24675
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8118
81.2%
0.0 107
 
1.1%
(Missing) 1775
 
17.8%

Length

2024-09-10T15:34:49.555467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:49.667312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8118
98.7%
0.0 107
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 8332
33.8%
. 8225
33.3%
1 8118
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8332
33.8%
. 8225
33.3%
1 8118
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8332
33.8%
. 8225
33.3%
1 8118
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8332
33.8%
. 8225
33.3%
1 8118
32.9%

Phone Social Premium.instagram
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing6630
Missing (%)66.3%
Memory size78.2 KiB
1.0
3078 
0.0
 
292

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10110
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3078
30.8%
0.0 292
 
2.9%
(Missing) 6630
66.3%

Length

2024-09-10T15:34:49.783383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:49.887600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3078
91.3%
0.0 292
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 3662
36.2%
. 3370
33.3%
1 3078
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3662
36.2%
. 3370
33.3%
1 3078
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3662
36.2%
. 3370
33.3%
1 3078
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3662
36.2%
. 3370
33.3%
1 3078
30.4%

Phone Social Premium.isWABusiness
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing8427
Missing (%)84.3%
Memory size78.2 KiB
0.0
1428 
1.0
145 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4719
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1428
 
14.3%
1.0 145
 
1.5%
(Missing) 8427
84.3%

Length

2024-09-10T15:34:50.000006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:50.106292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1428
90.8%
1.0 145
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3001
63.6%
. 1573
33.3%
1 145
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3001
63.6%
. 1573
33.3%
1 145
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3001
63.6%
. 1573
33.3%
1 145
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3001
63.6%
. 1573
33.3%
1 145
 
3.1%

Phone Social Premium.jeevansaathi
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1829
Missing (%)18.3%
Memory size78.2 KiB
0.0
7757 
1.0
 
414

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24513
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7757
77.6%
1.0 414
 
4.1%
(Missing) 1829
 
18.3%

Length

2024-09-10T15:34:50.216832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:50.324195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7757
94.9%
1.0 414
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 15928
65.0%
. 8171
33.3%
1 414
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15928
65.0%
. 8171
33.3%
1 414
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15928
65.0%
. 8171
33.3%
1 414
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15928
65.0%
. 8171
33.3%
1 414
 
1.7%

Phone Social Premium.jiomart
Categorical

MISSING 

Distinct2
Distinct (%)0.5%
Missing9590
Missing (%)95.9%
Memory size78.2 KiB
0.0
226 
1.0
184 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1230
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 226
 
2.3%
1.0 184
 
1.8%
(Missing) 9590
95.9%

Length

2024-09-10T15:34:50.435208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:50.544316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 226
55.1%
1.0 184
44.9%

Most occurring characters

ValueCountFrequency (%)
0 636
51.7%
. 410
33.3%
1 184
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 636
51.7%
. 410
33.3%
1 184
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 636
51.7%
. 410
33.3%
1 184
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 636
51.7%
. 410
33.3%
1 184
 
15.0%

Phone Social Premium.microsoft
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1872
Missing (%)18.7%
Memory size78.2 KiB
0.0
6910 
1.0
1218 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24384
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6910
69.1%
1.0 1218
 
12.2%
(Missing) 1872
 
18.7%

Length

2024-09-10T15:34:50.665396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:50.773497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6910
85.0%
1.0 1218
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 15038
61.7%
. 8128
33.3%
1 1218
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15038
61.7%
. 8128
33.3%
1 1218
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15038
61.7%
. 8128
33.3%
1 1218
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15038
61.7%
. 8128
33.3%
1 1218
 
5.0%

Phone Social Premium.my11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing9998
Missing (%)> 99.9%
Memory size78.2 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0

Common Values

ValueCountFrequency (%)
0.0 2
 
< 0.1%
(Missing) 9998
> 99.9%

Length

2024-09-10T15:34:50.888344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:50.990829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4
66.7%
. 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4
66.7%
. 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4
66.7%
. 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4
66.7%
. 2
33.3%

Phone Social Premium.paytm
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1757
Missing (%)17.6%
Memory size78.2 KiB
1.0
6760 
0.0
1483 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24729
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6760
67.6%
0.0 1483
 
14.8%
(Missing) 1757
 
17.6%

Length

2024-09-10T15:34:51.097616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:51.205009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6760
82.0%
0.0 1483
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 9726
39.3%
. 8243
33.3%
1 6760
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9726
39.3%
. 8243
33.3%
1 6760
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9726
39.3%
. 8243
33.3%
1 6760
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9726
39.3%
. 8243
33.3%
1 6760
27.3%

Phone Social Premium.rummycircle
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size78.2 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 9999
> 99.9%

Length

2024-09-10T15:34:51.320437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:51.417410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Phone Social Premium.shaadi
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1779
Missing (%)17.8%
Memory size78.2 KiB
0.0
8077 
1.0
 
144

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24663
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8077
80.8%
1.0 144
 
1.4%
(Missing) 1779
 
17.8%

Length

2024-09-10T15:34:51.519805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:51.627221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8077
98.2%
1.0 144
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 16298
66.1%
. 8221
33.3%
1 144
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16298
66.1%
. 8221
33.3%
1 144
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16298
66.1%
. 8221
33.3%
1 144
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16298
66.1%
. 8221
33.3%
1 144
 
0.6%

Phone Social Premium.skype
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1785
Missing (%)17.8%
Memory size78.2 KiB
0.0
7002 
1.0
1213 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24645
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7002
70.0%
1.0 1213
 
12.1%
(Missing) 1785
 
17.8%

Length

2024-09-10T15:34:51.744459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:51.853141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7002
85.2%
1.0 1213
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 15217
61.7%
. 8215
33.3%
1 1213
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15217
61.7%
. 8215
33.3%
1 1213
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15217
61.7%
. 8215
33.3%
1 1213
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15217
61.7%
. 8215
33.3%
1 1213
 
4.9%

Phone Social Premium.toi
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1943
Missing (%)19.4%
Memory size78.2 KiB
0.0
6036 
1.0
2021 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24171
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 6036
60.4%
1.0 2021
 
20.2%
(Missing) 1943
 
19.4%

Length

2024-09-10T15:34:51.967564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:52.077086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6036
74.9%
1.0 2021
 
25.1%

Most occurring characters

ValueCountFrequency (%)
0 14093
58.3%
. 8057
33.3%
1 2021
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14093
58.3%
. 8057
33.3%
1 2021
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14093
58.3%
. 8057
33.3%
1 2021
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14093
58.3%
. 8057
33.3%
1 2021
 
8.4%

Phone Social Premium.whatsapp
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing8427
Missing (%)84.3%
Memory size78.2 KiB
1.0
1344 
0.0
229 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4719
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1344
 
13.4%
0.0 229
 
2.3%
(Missing) 8427
84.3%

Length

2024-09-10T15:34:52.195542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:52.305254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1344
85.4%
0.0 229
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 1802
38.2%
. 1573
33.3%
1 1344
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1802
38.2%
. 1573
33.3%
1 1344
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1802
38.2%
. 1573
33.3%
1 1344
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1802
38.2%
. 1573
33.3%
1 1344
28.5%

Phone Social Premium.yatra
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)11.1%
Missing9991
Missing (%)99.9%
Memory size78.2 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9
 
0.1%
(Missing) 9991
99.9%

Length

2024-09-10T15:34:52.416890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:52.524932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9
100.0%

Most occurring characters

ValueCountFrequency (%)
0 18
66.7%
. 9
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18
66.7%
. 9
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18
66.7%
. 9
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18
66.7%
. 9
33.3%

Phone Social Premium.zoho
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1782
Missing (%)17.8%
Memory size78.2 KiB
0.0
8213 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8213
82.1%
1.0 5
 
0.1%
(Missing) 1782
 
17.8%

Length

2024-09-10T15:34:52.635026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:52.757385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8213
99.9%
1.0 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 16431
66.6%
. 8218
33.3%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16431
66.6%
. 8218
33.3%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16431
66.6%
. 8218
33.3%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16431
66.6%
. 8218
33.3%
1 5
 
< 0.1%

phone_digitalage
Real number (ℝ)

Distinct1781
Distinct (%)17.8%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1652.938
Minimum-1
Maximum6311
Zeros2
Zeros (%)< 0.1%
Negative380
Negative (%)3.8%
Memory size78.2 KiB
2024-09-10T15:34:52.884453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile89.5
Q1809
median1988
Q32031
95-th percentile3322.75
Maximum6311
Range6312
Interquartile range (IQR)1222

Descriptive statistics

Standard deviation984.11425
Coefficient of variation (CV)0.59537276
Kurtosis0.85013589
Mean1652.938
Median Absolute Deviation (MAD)408
Skewness0.38499467
Sum16522768
Variance968480.85
MonotonicityNot monotonic
2024-09-10T15:34:53.044600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1998 2022
 
20.2%
1988 950
 
9.5%
2396 384
 
3.8%
-1 380
 
3.8%
809 134
 
1.3%
3497 114
 
1.1%
698 104
 
1.0%
1262 93
 
0.9%
2147 79
 
0.8%
1965 68
 
0.7%
Other values (1771) 5668
56.7%
ValueCountFrequency (%)
-1 380
3.8%
0 2
 
< 0.1%
5 1
 
< 0.1%
12 2
 
< 0.1%
23 7
 
0.1%
53 35
 
0.4%
54 1
 
< 0.1%
61 13
 
0.1%
62 3
 
< 0.1%
63 5
 
0.1%
ValueCountFrequency (%)
6311 4
 
< 0.1%
5970 1
 
< 0.1%
5324 47
0.5%
5306 1
 
< 0.1%
5265 2
 
< 0.1%
5248 1
 
< 0.1%
5234 1
 
< 0.1%
5223 1
 
< 0.1%
5167 1
 
< 0.1%
5108 1
 
< 0.1%

phone_nameMatchScore
Real number (ℝ)

Distinct1007
Distinct (%)10.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean55.645558
Minimum-1
Maximum100
Zeros2
Zeros (%)< 0.1%
Negative2913
Negative (%)29.1%
Memory size78.2 KiB
2024-09-10T15:34:53.208925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median73.571429
Q396
95-th percentile100
Maximum100
Range101
Interquartile range (IQR)97

Descriptive statistics

Standard deviation42.100616
Coefficient of variation (CV)0.75658539
Kurtosis-1.6056019
Mean55.645558
Median Absolute Deviation (MAD)26.428571
Skewness-0.37760521
Sum556233
Variance1772.4619
MonotonicityNot monotonic
2024-09-10T15:34:53.374207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 2913
29.1%
100 2450
24.5%
83.33333333 283
 
2.8%
90 123
 
1.2%
85.71428571 112
 
1.1%
91.66666667 112
 
1.1%
90.90909091 103
 
1.0%
87.5 82
 
0.8%
80 74
 
0.7%
88.88888889 69
 
0.7%
Other values (997) 3675
36.8%
ValueCountFrequency (%)
-1 2913
29.1%
0 2
 
< 0.1%
5 1
 
< 0.1%
5.405405405 1
 
< 0.1%
5.555555556 1
 
< 0.1%
5.714285714 1
 
< 0.1%
5.882352941 1
 
< 0.1%
6.25 1
 
< 0.1%
6.451612903 2
 
< 0.1%
6.666666667 3
 
< 0.1%
ValueCountFrequency (%)
100 2450
24.5%
97.95918367 1
 
< 0.1%
97.82608696 1
 
< 0.1%
97.77777778 1
 
< 0.1%
97.6744186 1
 
< 0.1%
97.61904762 1
 
< 0.1%
97.56097561 1
 
< 0.1%
97.43589744 1
 
< 0.1%
97.36842105 2
 
< 0.1%
97.2972973 3
 
< 0.1%
Distinct5
Distinct (%)0.1%
Missing6
Missing (%)0.1%
Memory size78.2 KiB
Medium
4599 
High
4141 
Low
1179 
Very High
 
44
Very Low
 
31

Length

Max length9
Median length8
Mean length4.8368021
Min length3

Characters and Unicode

Total characters48339
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowLow
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Medium 4599
46.0%
High 4141
41.4%
Low 1179
 
11.8%
Very High 44
 
0.4%
Very Low 31
 
0.3%
(Missing) 6
 
0.1%

Length

2024-09-10T15:34:53.545150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:53.676904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 4599
45.7%
high 4185
41.6%
low 1210
 
12.0%
very 75
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 8784
18.2%
e 4674
9.7%
M 4599
9.5%
d 4599
9.5%
u 4599
9.5%
m 4599
9.5%
H 4185
8.7%
g 4185
8.7%
h 4185
8.7%
L 1210
 
2.5%
Other values (6) 2720
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8784
18.2%
e 4674
9.7%
M 4599
9.5%
d 4599
9.5%
u 4599
9.5%
m 4599
9.5%
H 4185
8.7%
g 4185
8.7%
h 4185
8.7%
L 1210
 
2.5%
Other values (6) 2720
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8784
18.2%
e 4674
9.7%
M 4599
9.5%
d 4599
9.5%
u 4599
9.5%
m 4599
9.5%
H 4185
8.7%
g 4185
8.7%
h 4185
8.7%
L 1210
 
2.5%
Other values (6) 2720
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8784
18.2%
e 4674
9.7%
M 4599
9.5%
d 4599
9.5%
u 4599
9.5%
m 4599
9.5%
H 4185
8.7%
g 4185
8.7%
h 4185
8.7%
L 1210
 
2.5%
Other values (6) 2720
 
5.6%

Application Status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
APPROVED
6677 
DECLINED
3323 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPPROVED
2nd rowAPPROVED
3rd rowAPPROVED
4th rowAPPROVED
5th rowDECLINED

Common Values

ValueCountFrequency (%)
APPROVED 6677
66.8%
DECLINED 3323
33.2%

Length

2024-09-10T15:34:53.847323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T15:34:53.994860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
approved 6677
66.8%
declined 3323
33.2%

Most occurring characters

ValueCountFrequency (%)
P 13354
16.7%
E 13323
16.7%
D 13323
16.7%
A 6677
8.3%
R 6677
8.3%
O 6677
8.3%
V 6677
8.3%
C 3323
 
4.2%
L 3323
 
4.2%
I 3323
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 13354
16.7%
E 13323
16.7%
D 13323
16.7%
A 6677
8.3%
R 6677
8.3%
O 6677
8.3%
V 6677
8.3%
C 3323
 
4.2%
L 3323
 
4.2%
I 3323
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 13354
16.7%
E 13323
16.7%
D 13323
16.7%
A 6677
8.3%
R 6677
8.3%
O 6677
8.3%
V 6677
8.3%
C 3323
 
4.2%
L 3323
 
4.2%
I 3323
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 13354
16.7%
E 13323
16.7%
D 13323
16.7%
A 6677
8.3%
R 6677
8.3%
O 6677
8.3%
V 6677
8.3%
C 3323
 
4.2%
L 3323
 
4.2%
I 3323
 
4.2%

Interactions

2024-09-10T15:34:34.220166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.149760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.224259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.418403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.730854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.717330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.695337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.717265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.999850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.330193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.264563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.337614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.569478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.839839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.825287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.801894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.840020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:33.165436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.445670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.387059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.475198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.717265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.954094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.935292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.911301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.005279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:33.307072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.561166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.499682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.613867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.862646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.067688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.047344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.021301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.144568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:33.461820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.682351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.628884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.752521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.994794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.175107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.164052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.128169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.274587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:33.628121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.793040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.758667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.889643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.150107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.286201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.271061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.238295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.421143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:33.761320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.900449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:25.889002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.010796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.321720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.391237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.374837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.345124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.583805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:33.884569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:35.013868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.015981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.144715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.506534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.505816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.484627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.476627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.746999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.009772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:35.121564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:26.122316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:27.310355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:28.628940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:29.617257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:30.592869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:31.591311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:32.858437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T15:34:34.117637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-10T15:34:54.280985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ADDRESS TYPEAGEAPPLIED AMOUNTASSET CTGASSET MODEL NOApplication StatusDEALER IDDOBEMPLOY CONSTITUTIONEMPLOYER TYPEGENDERHDB BRANCH STATEMARITAL STATUSPRIMARY ASSET MAKEPhone Social Premium.amazonPhone Social Premium.byjusPhone Social Premium.flipkartPhone Social Premium.housingPhone Social Premium.indiamartPhone Social Premium.instagramPhone Social Premium.isWABusinessPhone Social Premium.jeevansaathiPhone Social Premium.jiomartPhone Social Premium.microsoftPhone Social Premium.paytmPhone Social Premium.shaadiPhone Social Premium.skypePhone Social Premium.toiPhone Social Premium.whatsappPhone Social Premium.zohoTOTAL ASSET COSTmobilephone_digitalagephone_nameMatchScorephone_phoneFootprintStrengthOverallvpa
ADDRESS TYPE1.0000.0930.0000.0650.0490.6180.0480.0320.3190.3820.0000.1050.1470.0400.1110.0440.0900.0890.0200.0000.0000.0230.0000.0770.0710.0230.0700.0680.0000.0200.0430.0000.0000.0630.0300.000
AGE0.0931.000-0.0120.052-0.0290.080-0.052-0.1620.0900.0790.1040.0410.5530.0320.1380.0280.2260.0360.0130.0610.0470.0360.0000.0460.1540.0310.0430.1020.0840.000-0.0100.1390.063-0.1530.0520.000
APPLIED AMOUNT0.000-0.0121.0000.2920.0640.0000.0310.0840.0000.0000.0450.0500.0500.1850.0170.0000.0280.0720.0000.0000.0000.0640.0180.0520.0150.0000.0510.0220.0000.0000.679-0.0090.0420.0510.0140.000
ASSET CTG0.0650.0520.2921.0000.3201.0000.1250.0670.1770.0990.2750.1770.0690.5620.1050.0660.0870.0820.0000.0900.0650.0000.0000.1170.0520.0100.1130.0000.0000.0000.4100.0260.0280.0450.0380.000
ASSET MODEL NO0.049-0.0290.0640.3201.0000.0680.0590.0330.0670.0440.0290.1380.0520.4530.0680.0480.0840.0490.0000.0480.0000.0170.0000.0650.0380.0000.0620.0280.0000.0000.0240.015-0.0400.0260.0090.022
Application Status0.6180.0800.0001.0000.0681.0000.0460.0000.0080.0760.0230.1040.0000.1110.0000.0000.0000.0230.0000.0000.0000.0000.0460.0000.0230.0060.0000.0000.0240.0051.0000.0210.0420.0950.0170.022
DEALER ID0.048-0.0520.0310.1250.0590.0461.000-0.0890.1480.0880.0840.2930.0460.1840.0300.0310.0400.0220.0080.0380.0000.0180.0000.0180.0070.0000.0120.0300.0000.000-0.013-0.0130.002-0.0350.0120.026
DOB0.032-0.1620.0840.0670.0330.000-0.0891.0000.1730.0980.0300.0910.1570.0530.1440.0460.1080.0890.0000.0120.0000.0530.0000.1330.0660.0460.1350.0820.0000.0250.1150.0230.0560.1680.0560.037
EMPLOY CONSTITUTION0.3190.0900.0000.1770.0670.0080.1480.1731.0000.9990.0420.3500.0960.0890.1100.0710.0760.1060.0070.0000.0000.0800.0000.1610.0760.0320.1490.0960.0000.0000.0730.0450.0310.1320.0840.116
EMPLOYER TYPE0.3820.0790.0000.0990.0440.0760.0880.0980.9991.0000.0380.2070.1040.0690.1110.0710.0810.1070.0060.0000.0000.0790.0000.1620.0900.0320.1500.0940.0000.0000.0540.0240.0330.0880.0520.058
GENDER0.0000.1040.0450.2750.0290.0230.0840.0300.0420.0381.0000.1570.1140.1400.0000.0260.0000.0140.0000.0000.0270.0500.0000.0000.0680.0200.0000.0260.0530.0000.0930.0270.0390.1480.0400.043
HDB BRANCH STATE0.1050.0410.0500.1770.1380.1040.2930.0910.3500.2070.1571.0000.0870.1590.1410.0530.1220.1370.0480.0000.0690.0870.0000.1250.1260.0460.1220.1200.0790.0000.1470.0920.1410.0790.0720.049
MARITAL STATUS0.1470.5530.0500.0690.0520.0000.0460.1570.0960.1040.1140.0871.0000.0460.1580.0320.1670.0540.0000.0500.0610.0640.0830.1020.1270.0720.0990.1000.0170.0000.0960.1060.0570.1490.0990.084
PRIMARY ASSET MAKE0.0400.0320.1850.5620.4530.1110.1840.0530.0890.0690.1400.1590.0461.0000.1020.0430.0830.0760.0000.0300.0000.0290.0310.0990.0000.0000.0970.0270.0000.0000.3740.0080.0300.0220.0370.000
Phone Social Premium.amazon0.1110.1380.0170.1050.0680.0000.0300.1440.1100.1110.0000.1410.1580.1021.0000.1530.3810.1350.0360.0270.0710.1080.1320.2250.2860.0610.2300.2310.1850.0000.0710.0420.1390.2570.4240.150
Phone Social Premium.byjus0.0440.0280.0000.0660.0480.0000.0310.0460.0710.0710.0260.0530.0320.0430.1531.0000.1690.0860.0000.0290.0000.0330.0000.1460.1100.0250.1420.1490.0700.0220.0000.0000.0740.1170.1560.072
Phone Social Premium.flipkart0.0900.2260.0280.0870.0840.0000.0400.1080.0760.0810.0000.1220.1670.0830.3810.1691.0000.1190.0150.0580.0000.0940.2240.1900.3270.0560.1890.2420.2660.0000.0860.0370.1290.3130.4700.116
Phone Social Premium.housing0.0890.0360.0720.0820.0490.0230.0220.0890.1060.1070.0140.1370.0540.0760.1350.0860.1191.0000.0000.0300.0160.1240.1630.1790.0960.0770.1770.1060.1020.0430.0320.0430.0370.0970.1570.150
Phone Social Premium.indiamart0.0200.0130.0000.0000.0000.0000.0080.0000.0070.0060.0000.0480.0000.0000.0360.0000.0150.0001.0000.1360.0000.0000.2080.0000.0250.0000.0110.0160.0000.0000.0000.0000.0000.0390.0320.000
Phone Social Premium.instagram0.0000.0610.0000.0900.0480.0000.0380.0120.0000.0000.0000.0000.0500.0300.0270.0290.0580.0300.1361.0000.0310.0000.1680.0090.0290.0000.0000.0430.1500.0000.0710.0370.0000.0730.1530.099
Phone Social Premium.isWABusiness0.0000.0470.0000.0650.0000.0000.0000.0000.0000.0000.0270.0690.0610.0000.0710.0000.0000.0160.0000.0311.0000.0000.0000.0260.0280.0000.0320.0550.1260.0000.0000.0000.0650.0000.0000.042
Phone Social Premium.jeevansaathi0.0230.0360.0640.0000.0170.0000.0180.0530.0800.0790.0500.0870.0640.0290.1080.0330.0940.1240.0000.0000.0001.0000.1420.1090.0780.0950.1040.1000.0510.0000.0240.0310.0380.0790.1220.091
Phone Social Premium.jiomart0.0000.0000.0180.0000.0000.0460.0000.0000.0000.0000.0000.0000.0830.0310.1320.0000.2240.1630.2080.1680.0000.1421.0000.1000.0650.1030.1380.1550.0740.0000.0000.0750.0910.0000.1670.126
Phone Social Premium.microsoft0.0770.0460.0520.1170.0650.0000.0180.1330.1610.1620.0000.1250.1020.0990.2250.1460.1900.1790.0000.0090.0260.1090.1001.0000.1370.0610.9860.1970.0900.0370.0840.0540.1160.1570.2210.097
Phone Social Premium.paytm0.0710.1540.0150.0520.0380.0230.0070.0660.0760.0900.0680.1260.1270.0000.2860.1100.3270.0960.0250.0290.0280.0780.0650.1371.0000.0580.1350.1760.2470.0000.0000.0360.1010.3670.4390.157
Phone Social Premium.shaadi0.0230.0310.0000.0100.0000.0060.0000.0460.0320.0320.0200.0460.0720.0000.0610.0250.0560.0770.0000.0000.0000.0950.1030.0610.0581.0000.0620.0430.0000.0000.0690.0000.0000.0550.0580.087
Phone Social Premium.skype0.0700.0430.0510.1130.0620.0000.0120.1350.1490.1500.0000.1220.0990.0970.2300.1420.1890.1770.0110.0000.0320.1040.1380.9860.1350.0621.0000.1960.0890.0220.0770.0560.1160.1530.2170.098
Phone Social Premium.toi0.0680.1020.0220.0000.0280.0000.0300.0820.0960.0940.0260.1200.1000.0270.2310.1490.2420.1060.0160.0430.0550.1000.1550.1970.1760.0430.1961.0000.1240.0230.0630.0270.0880.1710.2080.097
Phone Social Premium.whatsapp0.0000.0840.0000.0000.0000.0240.0000.0000.0000.0000.0530.0790.0170.0000.1850.0700.2660.1020.0000.1500.1260.0510.0740.0900.2470.0000.0890.1241.0000.0000.0000.0560.1270.3500.2810.000
Phone Social Premium.zoho0.0200.0000.0000.0000.0000.0050.0000.0250.0000.0000.0000.0000.0000.0000.0000.0220.0000.0430.0000.0000.0000.0000.0000.0370.0000.0000.0220.0230.0001.0000.0000.0000.0240.0000.0650.000
TOTAL ASSET COST0.043-0.0100.6790.4100.0241.000-0.0130.1150.0730.0540.0930.1470.0960.3740.0710.0000.0860.0320.0000.0710.0000.0240.0000.0840.0000.0690.0770.0630.0000.0001.000-0.0120.0390.0670.0000.037
mobile0.0000.139-0.0090.0260.0150.021-0.0130.0230.0450.0240.0270.0920.1060.0080.0420.0000.0370.0430.0000.0370.0000.0310.0750.0540.0360.0000.0560.0270.0560.000-0.0121.0000.1000.0660.0540.030
phone_digitalage0.0000.0630.0420.028-0.0400.0420.0020.0560.0310.0330.0390.1410.0570.0300.1390.0740.1290.0370.0000.0000.0650.0380.0910.1160.1010.0000.1160.0880.1270.0240.0390.1001.0000.0780.2820.046
phone_nameMatchScore0.063-0.1530.0510.0450.0260.095-0.0350.1680.1320.0880.1480.0790.1490.0220.2570.1170.3130.0970.0390.0730.0000.0790.0000.1570.3670.0550.1530.1710.3500.0000.0670.0660.0781.0000.2260.026
phone_phoneFootprintStrengthOverall0.0300.0520.0140.0380.0090.0170.0120.0560.0840.0520.0400.0720.0990.0370.4240.1560.4700.1570.0320.1530.0000.1220.1670.2210.4390.0580.2170.2080.2810.0650.0000.0540.2820.2261.0000.099
vpa0.0000.0000.0000.0000.0220.0220.0260.0370.1160.0580.0430.0490.0840.0000.1500.0720.1160.1500.0000.0990.0420.0910.1260.0970.1570.0870.0980.0970.0000.0000.0370.0300.0460.0260.0991.000

Missing values

2024-09-10T15:34:35.375818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-10T15:34:35.783310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DEALER IDAPPLICATION LOGIN DATEHDB BRANCH NAMEHDB BRANCH STATEFIRST NAMEMIDDLE NAMELAST NAMEmobileAADHAR VERIFIEDCibil ScoreMOBILE VERIFICATIONDEALER NAMETOTAL ASSET COSTASSET CTGASSET MODEL NOAPPLIED AMOUNTPRIMARY ASSET MAKEPrimary Asset Model NoPersonal Email AddressMARITAL STATUSGENDERDOBAGEADDRESS TYPEEMPLOY CONSTITUTIONEMPLOYER NAMEEMPLOYER TYPEPan Namenamevpaupi_namePhone Social Premium.a23gamesPhone Social Premium.amazonPhone Social Premium.byjusPhone Social Premium.flipkartPhone Social Premium.housingPhone Social Premium.indiamartPhone Social Premium.instagramPhone Social Premium.isWABusinessPhone Social Premium.jeevansaathiPhone Social Premium.jiomartPhone Social Premium.microsoftPhone Social Premium.my11Phone Social Premium.paytmPhone Social Premium.rummycirclePhone Social Premium.shaadiPhone Social Premium.skypePhone Social Premium.toiPhone Social Premium.whatsappPhone Social Premium.yatraPhone Social Premium.zohophone_digitalagephone_nameMatchScorephone_phoneFootprintStrengthOverallApplication Status
010698907/20/2022DELHI-SFDELHISUNILNaNCHANDER9210574080NO726TrueV D AUTO WHEELS CHHOTIAL95041.0MCEXA13954285000HONDA MOTORSSHINE DRUM BSVISUNILSEHRAWAT7355@GMAIL.COMMarriedMale104197844ParentalSALARIEDsehrawat spare partsSALARIEDSUNIL KUMARSUNIL CHANDERABFSPAYSUNIL KUMARNaN1.00.01.00.01.0NaNNaN0.0NaN0.0NaN1.0NaN0.00.01.0NaNNaN0.05324.067.222222HighAPPROVED
110897507/28/2022PATNA-SFBIHARAMRITNaNKUMAR8877987018NONaNTrueCHANDAN AUTOMOBILES 259 KGS TOWERNaNNaN14020890000HERO MOTORSSPLENDOR PLUS SELF DRUM BSVI I3SNULL@GMAIL.COMNaNMale101199725NaNNaNNaNNaNAMRIT KUMARAMRIT KUMAROKSBIAmrit KumarNaN1.01.01.00.01.0NaNNaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.01998.0100.000000HighAPPROVED
211100407/15/2022DARJEELING-SFWEST BENGALANIMESHNaNTHAPA8910862135NO737TrueKN VISION 53HILL CART ROAD119436.0SCEXA16000175000TVS MOTOR COTVS NTORQ SUPER SQUAD EDITION BSVICHETTRIDIKSHA@GMAIL.COMSingleMale908199922Self/Spouse OwnedSELF-EMPLOYEDThapas shopSELF-EMPLOYEDANIMESH THAPAANIMESH THAPANaNNaNNaN0.00.01.00.01.0NaNNaN0.0NaN0.0NaN0.0NaN0.00.00.0NaNNaN0.0-1.0-1.000000LowAPPROVED
319202007/04/22SAHARANPUR-SFUTTAR PRADESHADITYANaNSINGH9758428017NO713TrueMAHADEV AUTOMOBILES MANGLAUR87000.0MCECA16083278500HERO MOTORSSPLENDOR+ BLK ACCT SS DRUM I3S BSVIADITYA98@GAMIL.COMSingleMale307199824ParentalSELF-EMPLOYEDagriculture farmingSELF-EMPLOYEDADITYA KUMARADITYA SINGHPAYTMAditya KumarNaN0.00.01.01.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.01.0NaNNaN0.01998.072.777778HighAPPROVED
45509507/15/2022MODASA-SFGUJARATPARMARHARESHBHAIAMRUTBHAI9687028486NO669TrueDWARKESH AUTO SHAMLAJI ROADNaNNaN17464170000HONDA MOTORSDIO STD BSVIPARMARHARESHBHAI1989@GMAIL.COMNaNMale507198933NaNNaNNaNNaNHARESHBHAI AMRUTBHAI PARMARPARMAR HARESHBHAI AMRUTBHAIIKWIKHARESHBHAI AMRUTBHAINaN1.00.00.00.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.01.0NaNNaN0.01998.068.095238HighDECLINED
520055807/26/2022RAMPUR-SFNaNAAYSHAABDULLATEEF9720733482NONaNTrueR K MOTORS NANITAL ROADNaNNaN198364107000HERO MOTORSDESTINI 125 XTEC ALLOY BSVIAAYSHA123@GMAIL.COMNaNFemale101199032NaNNaNNaNNaNAAYSHAAAYSHA ABDUL LATEEFAXLAYSHA ANaN1.00.01.00.01.0NaNNaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.01998.073.015873HighAPPROVED
610039807/25/2022DELHI EAST-SFDELHIROYELNaNHAZARI8287660919NONaNTrueG K MOTORS P L VIKAS MARG112562.0MCEXA17890399999TVS MOTOR CORAIDER DISCROYEL123@GMAIL.COMMarriedMale203199230RentedSALARIEDONE RAJASALARIEDROYEL HAZARIROYEL HAZARIIBLROYEL HAZARINaN1.00.01.00.01.0NaNNaN0.0NaN1.0NaN1.0NaN0.01.00.0NaNNaN0.0750.0100.000000MediumAPPROVED
78694207/21/2022MIRZAPUR-SFUTTAR PRADESHMOHMMADNaNASHIF8787011013NO762TrueKRISHNA MANI MOTORS PILIKOTHINaNNaN143607102000BAJAJ AUTO INDIAPULSAR 125 DISC CBS BSVIMOHMMAD@GMAIL.COMNaNMale140419890NaNNaNNaNNaNMOHMMAD ASHIFMOHMMAD ASHIFIBLMOHMMAD ASHIFNaN1.00.01.00.01.0NaNNaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.01998.0100.000000HighDECLINED
88078707/04/22JIND-SFHARYANADEEPAKNaNRAMMEHAR9992177321NONaNTrueBANSAL AUTO SALES ROHTAK ROAD JIND78120.0MCECA13838372500BAJAJ AUTO INDIAPLATINA 100 ES ALLOY BSVIDEEPAK@GMAIL.COMMarriedMale1004199824RESIDENCESELF-EMPLOYEDagricultureSELF-EMPLOYEDDEEPAKDEEPAK RAMMEHARPAYTMDeepak .NaN0.00.01.00.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.01.0NaNNaN0.01998.079.411765HighAPPROVED
919843107/25/2022NAWADA-SFBIHARSURAJNaNRAJVANSHI7258912035NONaNTrueRAMACHANDRA PROJECTS P L NEAR ITI N109500.0MCEXA14966890000BAJAJ AUTO INDIAPULSAR 125 SPLIT SEAT BSVISURAJK01253@GMAIL.COMSingleMale906199725Self/Spouse OwnedSELF-EMPLOYEDSuraj electric shop govindpurSELF-EMPLOYEDSURAJ KUMARSURAJ RAJVANSHIOKICICISURAJ KUMAR S O BINDA RAJWANSHINaN1.00.01.00.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.01988.071.759259HighAPPROVED
DEALER IDAPPLICATION LOGIN DATEHDB BRANCH NAMEHDB BRANCH STATEFIRST NAMEMIDDLE NAMELAST NAMEmobileAADHAR VERIFIEDCibil ScoreMOBILE VERIFICATIONDEALER NAMETOTAL ASSET COSTASSET CTGASSET MODEL NOAPPLIED AMOUNTPRIMARY ASSET MAKEPrimary Asset Model NoPersonal Email AddressMARITAL STATUSGENDERDOBAGEADDRESS TYPEEMPLOY CONSTITUTIONEMPLOYER NAMEEMPLOYER TYPEPan Namenamevpaupi_namePhone Social Premium.a23gamesPhone Social Premium.amazonPhone Social Premium.byjusPhone Social Premium.flipkartPhone Social Premium.housingPhone Social Premium.indiamartPhone Social Premium.instagramPhone Social Premium.isWABusinessPhone Social Premium.jeevansaathiPhone Social Premium.jiomartPhone Social Premium.microsoftPhone Social Premium.my11Phone Social Premium.paytmPhone Social Premium.rummycirclePhone Social Premium.shaadiPhone Social Premium.skypePhone Social Premium.toiPhone Social Premium.whatsappPhone Social Premium.yatraPhone Social Premium.zohophone_digitalagephone_nameMatchScorephone_phoneFootprintStrengthOverallApplication Status
99909420007/07/22JAIPUR-SFRAJASTHANBIRUNaNRAM8219871676NONaNTrueCHOUDHARY AUTOMOBILES KOTPUTLI87936.0MCECA14020890000HERO MOTORSSPLENDOR PLUS SELF DRUM BSVI I3SBIRU123@GMAIL.COMMarriedMale101199428Self/Spouse OwnedSELF-EMPLOYEDbiru general StoreSELF-EMPLOYEDBIRUBIRU RAMNaNNaNNaN0.00.0NaN0.01.0NaNNaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.02758.0-1.000000MediumAPPROVED
99918431507/24/2022HYDERABAD-SFTELANGANASHAIKNaNAFZAL9391101226NO713TrueSRI VAISHNAOI AUTOMOBILS IND PL KRINaNNaN12464890000HONDA MOTORSACTIVA 125 DISC(BSVI)SHAIKAFZAL@GMAIL.COMNaNMale907197151NaNNaNNaNNaNSHAIK AFZALSHAIK AFZALYBLMr SHAIK AFZALNaN0.00.00.00.01.01.0NaN0.0NaN0.0NaN0.0NaN0.00.01.0NaNNaN0.01998.0100.000000MediumAPPROVED
999211200607/29/2022NOIDA-SFUTTAR PRADESHIMRANNaNKHAN8510845233NO741TrueDHANSRI MOTOCORP ECOTECHNaNNaN19954698000HERO MOTORSSPLENDOR PLUS XTEC BSVINIL@GMAIL.COMNaNMale101199230NaNNaNNaNNaNIMRAN KHANIMRAN KHANYBLIMRAN KHANNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN265.0100.000000LowDECLINED
999310373307/22/2022DELHI-SFDELHIJHAROKHANaNDEVI7632082837NONaNTrueGD MOTORS PVT LTD TILAK NAGARNaNNaN16021699999SUZUKI MOTORCYCLEBURGMAN STREET BLUETOOTH BSVIJHAROKHADEVI86@GMAIL.COMNaNFemale101198636NaNNaNNaNNaNJHAROKHA DEVIJHAROKHA DEVINaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2396.0-1.000000LowDECLINED
999410511107/28/2022TEZPUR-SFASSAMRITANaNGHANCHI8876152320NONaNTrueRUDRA MOTORS LAWKHOWA ROAD96470.0SCECA18308696471HERO MOTORSPLEASURE PLUS XTEC DRUM BSVIRITA@GMAIL.COMMarriedFemale101199428Self/Spouse OwnedSELF-EMPLOYEDRITA STORESELF-EMPLOYEDRITA NAYAKRITA GHANCHINaNNaNNaN0.00.01.00.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.0677.0-1.000000MediumAPPROVED
999510510107/11/22FARRUKHABAD-SFUTTAR PRADESHAJAYNaNNARESH8400644964NONaNTrueGUPTA AUTO DEALERS BARHPUR88970.0MCECA16083287000HERO MOTORSSPLENDOR+ BLK ACCT SS DRUM I3S BSVIAJAYDEVSHAKYA@GMAIL.COMMarriedMale1204200022ParentalSALARIEDGROWFAST ORGANIC DIAMAOND PVT LTDSALARIEDAJAY DEVAJAY NARESHOKICICIAJAY DEVNaN1.01.00.00.01.01.0NaN0.0NaN1.0NaN1.0NaN0.01.01.0NaNNaN0.0-1.060.576923MediumAPPROVED
99968505407/04/22BHAGALPUR-SFBIHARSURESHKUMARPRASAD9708883564NONaNTrueRAMESHWARAM ENTERPRISES DR R P ROAD120000.0MCEXA18308590000TVS MOTOR CORAIDER 125 DISC BSVISURESHWISHKARMA356@GMAIL.COMMarriedMale210197150Self/Spouse OwnedSELF-EMPLOYEDRaj Ayurved centreSELF-EMPLOYEDSURESH KUMAR VISHWAKARMASURESH KUMAR PRASADYBLMR SURESH KUMAR VISHWAKARMANaN1.00.01.00.01.00.00.00.0NaN0.0NaN1.0NaN0.00.00.00.0NaN0.01998.071.078431HighAPPROVED
99975371007/10/22LUDHIANA-SFPUNJABSANJAYNaNAAGAN9888532016NONaNTrueSHREE DADU AUTOS P L GANDHI NGR89929.0SCECA14324089000HONDA MOTORSACTIVA 6G DLX BSVISANJAY@GMAIL.COMMarriedMale101198339RentedSELF-EMPLOYEDSanjay karyana storeSELF-EMPLOYEDSANJAY AAGANSANJAY AAGANOKSBISANJAY AAGAN SO GOBINDA AAGANNaN0.01.00.00.01.0NaN0.00.0NaN0.0NaN1.0NaN0.00.00.01.0NaN0.01988.0100.000000MediumAPPROVED
99988924007/29/2022MEERUT-SFUTTAR PRADESHSANJAYNaNSINGH8923338426NONaNTrueSHREE SHIV SHAKTI AUTOMOTIVE RORKRD89340.0MCECA16083285000HERO MOTORSSPLENDOR+ BLK ACCT SS DRUM I3S BSVINOMAIL@GMAIL.COMMarriedMale1912197051Self/Spouse OwnedSELF-EMPLOYEDSANJAY MILK DARYSELF-EMPLOYEDSANJAY KUMARSANJAY SINGHNaNNaNNaN0.00.00.00.01.0NaNNaN0.0NaN0.0NaN0.0NaN0.00.00.0NaNNaN0.01096.0-1.000000LowAPPROVED
999910945507/14/2022ALIGARH-SFUTTAR PRADESHSAMEERNaNSALEEM6396509887NO761TrueMASCOT AUTOMOTIVE INDIA P L INDUST101262.0MCEXA12910285000HONDA MOTORSSP 125 DRUM BS VISAMEER.KHAN12345@GMAIL.COMMarriedMale101199725ParentalSALARIEDalana meet factorySALARIEDSAMEER KHANSAMEER SALEEMOKAXISSAMEER KHANNaN1.01.01.00.01.01.00.00.0NaN0.0NaN1.0NaN0.00.00.01.0NaN0.0809.066.239316HighAPPROVED

Duplicate rows

Most frequently occurring

DEALER IDAPPLICATION LOGIN DATEHDB BRANCH NAMEHDB BRANCH STATEFIRST NAMEMIDDLE NAMELAST NAMEmobileAADHAR VERIFIEDCibil ScoreMOBILE VERIFICATIONDEALER NAMETOTAL ASSET COSTASSET CTGASSET MODEL NOAPPLIED AMOUNTPRIMARY ASSET MAKEPrimary Asset Model NoPersonal Email AddressMARITAL STATUSGENDERDOBAGEADDRESS TYPEEMPLOY CONSTITUTIONEMPLOYER NAMEEMPLOYER TYPEPan Namenamevpaupi_namePhone Social Premium.a23gamesPhone Social Premium.amazonPhone Social Premium.byjusPhone Social Premium.flipkartPhone Social Premium.housingPhone Social Premium.indiamartPhone Social Premium.instagramPhone Social Premium.isWABusinessPhone Social Premium.jeevansaathiPhone Social Premium.jiomartPhone Social Premium.microsoftPhone Social Premium.my11Phone Social Premium.paytmPhone Social Premium.rummycirclePhone Social Premium.shaadiPhone Social Premium.skypePhone Social Premium.toiPhone Social Premium.whatsappPhone Social Premium.yatraPhone Social Premium.zohophone_digitalagephone_nameMatchScorephone_phoneFootprintStrengthOverallApplication Status# duplicates
08581407/12/22LALGANJ-SFUTTAR PRADESHFARHANNaNAHMAD9793879701NONaNTrueHINDUSTAN AUTO MOBILES RETWANaNNaN13954695000TVS MOTOR CONTORQ RACE EDITION BSVIAHMADFARHAN2214@GMAIL.COMNaNMale101199725RESIDENCENaNNaNNaNNaNFARHAN AHMADOKHDFCBANKFARHAN SO JAVED AHMADNaN1.00.01.00.01.01.00.00.0NaN0.0NaN1.0NaN1.00.00.01.0NaN0.0486.085.714286HighDECLINED2
18743807/28/2022JORHAT-SFASSAMTILOKNaNBASFOR6003587172NO734TrueGDCL AGENCIES P L SONARINaNNaN185293100000SUZUKI MOTORCYCLEAVENIS RACE ED BSVINULLI@GMAIL.COMNaNMale208199130NaNNaNNaNNaNTILOK BASFORTILOK BASFOROKSBISumit BasforNaN1.00.01.00.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.0NaNNaNNaN0.0668.063.492063HighDECLINED2
29158307/08/22DELHI SOUTH-SFDELHIKRISHNANANDANKUMAR9534011151NONaNTrueOM SONS PROJECTS P L ADCHININaNNaN12910199000HONDA MOTORSSP 125 DISC BS VINULL@GMAIL.COMNaNMale1012199328RESIDENCENaNNaNNaNKRISHNA NANDAN KUMARKRISHNA NANDAN KUMAROKHDFCBANKKRISHNA NANDAN KUMARNaN1.00.01.00.01.0NaNNaN0.0NaN1.0NaN1.0NaN0.01.01.0NaNNaN0.02396.090.000000HighDECLINED2
39196907/04/22HALDWANI-SFUTTARAKHANDMOHDNaNRAFAT9639208194NONaNTrueSHRI BALAJI MOTORS BAREILLY ROADNaNNaN143240100000HONDA MOTORSACTIVA 6G DLX BSVINULL78@GMAIL.COMNaNMale101198141RESIDENCENaNNaNNaNMOHD RAFATMOHD RAFATOKICICIMOHD RAFATNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN281.0100.000000LowDECLINED2
410570707/31/2022ROORKEE-SFUTTARAKHANDSONUNaNKUMAR7248847715NONaNTrueUP AUTOMOBILES S 23 24 AVAS VIKASNaNNaN14021290000HERO MOTORSSPLENDOR PLUS SELF START DRUM BSVINULL@GMAIL.COMNaNMale2502198438NaNNaNNaNNaNNaNSONU KUMARNaNNaNNaN0.00.00.00.01.00.00.00.0NaN0.0NaN0.0NaN0.00.00.01.0NaN0.0-1.0-1.000000LowDECLINED2
510718707/12/22DHAMPUR-SFNaNABDULNaNKADIR6397903454NONaNTrueSAMRAT ENTERPRISES SUGAR MILLNaNNaN133253106000SUZUKI MOTORCYCLENEW ACCESS 125 DRUM CBS BSVIKADIR454@GMAIL.COMNaNMale101199824RESIDENCENaNNaNNaN-ABDUL KADIRNaNNaNNaN0.00.01.00.01.00.0NaN0.0NaN0.0NaN0.0NaN0.00.00.0NaNNaN0.01988.0-1.000000MediumDECLINED2
610751907/29/2022LALITPUR-SFUTTAR PRADESHRAMJINaNLODHI9116257288NO660TrueSANJAY AUTOMOBILES JHANSI ROADNaNNaN160892150000TVS MOTOR COSPORT KICK START SPOKE BSVIRAMJI@GMAIL.COMNaNMale101198438NaNNaNNaNNaNRAMJI LODHIRAMJI LODHINaNNaNNaN0.00.00.00.01.00.0NaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.02657.0-1.000000MediumDECLINED2
710770007/31/2022DELHI-SFDELHISHOBHANaNDEVI9654941564NO712TrueK K AUTOMOBILES TILANG PURNaNNaN13489888500TVS MOTOR COJUPITER BSVISHOBHA2000@GMAIL.COMNaNFemale1507200022NaNNaNNaNNaNSHOBHA DEVISHOBHA DEVIIBLSHOBHA DEVINaN0.00.00.00.01.00.00.00.0NaN0.0NaN1.0NaN0.00.00.01.0NaN0.01503.0100.000000MediumAPPROVED2
811058807/28/2022INDAPUR-SFMAHARASHTRASAMBHAJITUKARAMBHONG9890577452NO770TrueSAI MOTORS TEMBHURNI AKLUJ CHOUKNaNNaN14966899000BAJAJ AUTO INDIAPULSAR 125 SPLIT SEAT BSVISAGARPGAIKWAD28@GMAIL.COMNaNMale103197349NaNNaNNaNNaN-SAMBHAJI TUKARAM BHONGNaNNaNNaN0.00.00.00.01.01.0NaN0.0NaN0.0NaN1.0NaN0.00.00.0NaNNaN0.04117.0-1.000000MediumDECLINED2